Manuscript 1 from Fernanda Hansen P. de Moraes Thesis - Cortical folding alterations in humans due to aging and diseases
Fernanda Hansen Pacheco de Moraes
1 dec 2021
- 1 set seed for random process
- 2 Deaging
- 3 Data description
- 4 Results
- 5 Figure 1 A
- 6 Figure 1
- 7 Figure S1
- 8 Figure S2
- 9 Figure 2
- 10 Table S2
- 11 Table 1
Description of the procedures and analysis present in Manuscript 1, Independent morphological correlates to aging, Mild Cognitive Impairment, and Alzheimer’s Disease, at the Doctorate Thesis presented to the Programa de Pós-Graduação em Ciências Médicas at the Instituto D’Or de Pesquisa e Ensino as a partial requirement to obtain the Doctorate Degree.
Part of the data used here cannot be shared due to restrictions of the Ethic Committee. Data can be shared upon reasonable request to the corresponding author. To fulfill these limitation, we will generate random data to simulate the results.
Get in touch with us (fernandahmoraes@gmail.com) in case any help is needed, our aim is to improve the code as needed!
setwd("~/GitHub/CorticalFolding_AD_Aging")## define functions
# test angular coeficinet versus theoretical value
test_coef <- function(reg, coefnum, val){
co <- coef(summary(reg))
tstat <- (co[coefnum,1] - val)/co[coefnum,2]
2 * pt(abs(tstat), reg$df.residual, lower.tail = FALSE)
}
# wrap text
wrapper <- function(x, ...) paste(strwrap(x, ...), collapse = "\n")library(readr)
library(tidyverse)
library(lubridate)
library(ggpubr)
library(kableExtra)
library(broom)
library(MASS)
library(cutpointr)
library(ggstatsplot)
library(effects)# COLOR BLIND PALETTE WITH BLACK
cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")1 set seed for random process
set.seed(1)dados_raw <- read_csv("dados_raw.csv")# estimate cortical folding variables
dados_raw <- dados_raw %>%
mutate(
# create new variables
logAvgThickness = log10(AvgThickness),
logTotalArea = log10(TotalArea),
logExposedArea = log10(ExposedArea),
localGI = TotalArea / ExposedArea,
k = sqrt(AvgThickness) * TotalArea / (ExposedArea ^ 1.25),
K = 1 / 4 * log10(AvgThickness ^ 2) + log10(TotalArea) - 5 / 4 * log10(ExposedArea),
S = 3 / 2 * log10(TotalArea) + 3 / 4 * log10(ExposedArea) - 9 / 4 * log10(AvgThickness ^
2) ,
I = log10(TotalArea) + log10(ExposedArea) + log10(AvgThickness ^ 2),
c = as.double(ifelse(
ROI == "hemisphere", NA, 4 * pi / GaussianCurvature
)),
TotalArea_corrected = ifelse(ROI == "hemisphere", TotalArea, TotalArea * c),
ExposedArea_corrected = ifelse(ROI == "hemisphere", ExposedArea, ExposedArea * c),
logTotalArea_corrected = log10(TotalArea_corrected),
logExposedArea_corrected = log10(ExposedArea_corrected),
localGI_corrected = ifelse(
ROI == "hemisphere",
TotalArea / ExposedArea,
TotalArea_corrected / ExposedArea_corrected
),
k_corrected = ifelse(
ROI == "hemisphere",
sqrt(AvgThickness) * log10(TotalArea) / (log10(ExposedArea) ^ 1.25),
sqrt(AvgThickness) * log10(TotalArea_corrected) / (log10(ExposedArea_corrected ^
1.25))
),
K_corrected = ifelse(
ROI == "hemisphere",
1 / 4 * log10(AvgThickness ^ 2) + log10(TotalArea) - 5 / 4 * log10(ExposedArea),
1 / 4 * log10(AvgThickness ^ 2) + log10(TotalArea_corrected) - 5 / 4 * log10(ExposedArea_corrected)
),
I_corrected = ifelse(
ROI == "hemisphere",
log10(TotalArea) + log10(ExposedArea) + log10(AvgThickness ^ 2) ,
log10(TotalArea_corrected) + log10(ExposedArea_corrected) + log10(AvgThickness ^ 2)
),
S_corrected = ifelse(
ROI == "hemisphere",
3 / 2 * log10(TotalArea) + 3 / 4 * log10(ExposedArea) - 9 / 4 * log10(AvgThickness ^ 2) ,
3 / 2 * log10(TotalArea_corrected) + 3 / 4 * log10(ExposedArea_corrected) - 9 / 4 * log10(AvgThickness ^ 2)
),
Knorm = K_corrected / sqrt(1 + (1 / 4) ^ 2 + (5 / 4) ^ 2),
Snorm = S_corrected / sqrt((3 / 2) ^ 2 + (3 / 4) ^ 2 + (9 / 4) ^ 2),
Inorm = I_corrected / sqrt(1 ^ 2 + 1 ^ 2 + 1 ^ 1)
)
# create age intervals
dados_raw$Age_interval <- cut(dados_raw$Age,
breaks = c(0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100),
right = FALSE,
include.lowest = TRUE)
dados_raw$Age_interval10 <- cut(dados_raw$Age,
breaks = c(0, 10, 20, 30, 40, 50, 60, 70, 80, 90, 100),
right = FALSE,
include.lowest = TRUE)dados_all <- dados_raw %>% filter(
Diagnostic == "CONTROLE" |
Diagnostic == "CCL" |
Diagnostic == "ALZ", !is.na(logAvgThickness), ExposedArea != 0 | !is.na(localGI), !is.infinite(logExposedArea)) %>%
droplevels()
dados <- dados_all# rename diagnostics
dados$Diagnostic[dados$Diagnostic == "CONTROLE"] <- "CTL"
dados$Diagnostic[dados$Diagnostic == "ALZ"] <- "AD"
dados$Diagnostic[dados$Diagnostic == "CCL"] <- "MCI"
dados$Diagnostic <- factor(dados$Diagnostic, levels = c("AD", "MCI","CTL"))
# filter data
dados <- dados %>%
filter(machine == "Philips-Achieva", # include only subjects acquired at Philips Achieva 3T
ESC == 8 | ESC > 8, # include only subjects with 8 years of scholarship or more
Session == 1) %>% # use only data from Session 1
droplevels() # delete factor levels2 Deaging
# define age for deaging
Age.cor = 25
## Avg thickness ----
decay_AvgThickness <-
filter(
dados,
Diagnostic == "CTL",!is.na(TotalArea),!is.nan(TotalArea),!is.infinite(TotalArea)
) %>%
droplevels() %>%
group_by(ROI) %>%
do(fit_decay_AvgThickness = tidy(rlm(AvgThickness ~ Age, data = .), conf.int =
TRUE)) %>%
unnest(cols = c(fit_decay_AvgThickness)) %>%
filter(term == "Age") %>%
mutate(c_AvgThickness = estimate,
std_error_c_AvgThickness = std.error) %>%
dplyr::select(c(ROI, c_AvgThickness, std_error_c_AvgThickness))
## TotalArea ----
decay_TotalArea <-
filter(
dados,
Diagnostic == "CTL",
!is.na(TotalArea),
!is.nan(TotalArea),
!is.infinite(TotalArea)
) %>%
droplevels() %>%
group_by(ROI) %>%
do(fit_decay_TotalArea = tidy(rlm(TotalArea ~ Age, data = .), conf.int =
TRUE)) %>%
unnest(cols = c(fit_decay_TotalArea)) %>%
filter(term == "Age") %>%
mutate(c_TotalArea = estimate,
std_error_c_TotalArea = std.error) %>%
dplyr::select(c(ROI, c_TotalArea, std_error_c_TotalArea))
## ExposedArea ----
decay_ExposedArea <-
filter(
dados,
Diagnostic == "CTL",
!is.na(ExposedArea),
!is.nan(ExposedArea),
!is.infinite(ExposedArea)
) %>%
droplevels() %>%
group_by(ROI) %>%
do(fit_decay_ExposedArea = tidy(rlm(ExposedArea ~ Age, data = .), conf.int = TRUE)) %>%
unnest(cols = c(fit_decay_ExposedArea)) %>%
filter(term == "Age") %>%
mutate(c_ExposedArea = estimate,
std_error_c_ExposedArea = std.error) %>%
dplyr::select(c(ROI, c_ExposedArea, std_error_c_ExposedArea))
## join
dados <- full_join(dados, decay_AvgThickness) %>%
full_join(decay_TotalArea) %>%
full_join(decay_ExposedArea) %>%
mutate(
AvgThickness_age_decay = AvgThickness - c_AvgThickness * (Age - Age.cor),
logAvgThickness_age_decay = log10(AvgThickness_age_decay),
TotalArea_age_decay = TotalArea - c_TotalArea * (Age - Age.cor),
logTotalArea_age_decay = log10(TotalArea_age_decay),
ExposedArea_age_decay = ExposedArea - c_ExposedArea * (Age - Age.cor),
logExposedArea_age_decay = log10(ExposedArea_age_decay),
K_age_decay = log10(TotalArea_age_decay) + 1/4*log10(AvgThickness_age_decay^2) - 5/4*log10(ExposedArea_age_decay),
I_age_decay = log10(TotalArea_age_decay) + log10(ExposedArea_age_decay) + log10(AvgThickness_age_decay^2),
S_age_decay = 3/2*log10(TotalArea_age_decay) + 3/4*log10(ExposedArea_age_decay) - 9/4*log10(AvgThickness_age_decay^2))
dados$logAvgThickness <- as.double(dados$logAvgThickness)
dados$logExposedArea <- as.double(dados$logExposedArea)
dados$logTotalArea <- as.double(dados$logTotalArea)dados_v1 <- filter(dados, ROI == "F" | ROI == "T" | ROI == "O" | ROI == "P" | ROI == "hemisphere") %>%
droplevels()
# lobe data
dados_lobos_v1 <- unique(filter(dados, ROI == "F" | ROI == "T" | ROI == "O" | ROI == "P", !is.na(K_age_decay), SUBJ != "SUBJ211", SUBJ != "SUBJ223")) %>%
droplevels()
# hemisphere data
dados_hemi_v1 <- unique(filter(dados, ROI == "hemisphere", !is.na(K_age_decay)))3 Data description
| Diagnostic | N | age | age_range | ESC | T | AT | AE | k | K | S | I |
|---|---|---|---|---|---|---|---|---|---|---|---|
| AD | 13 | 77 ± 6.1 | 63 ; 86 | 13 ± 3 | 2.4 ± 0.079 | 95000 ± 9300 | 37000 ± 3000 | 0.28 ± 0.01 | -0.55 ± 0.015 | 9.2 ± 0.13 | 10 ± 0.069 |
| MCI | 33 | 72 ± 4.6 | 62 ; 82 | 13 ± 2.4 | 2.5 ± 0.085 | 97000 ± 8500 | 37000 ± 2800 | 0.29 ± 0.0096 | -0.53 ± 0.014 | 9.2 ± 0.12 | 10 ± 0.063 |
| CTL | 77 | 66 ± 8.4 | 43 ; 80 | 15 ± 2.2 | 2.5 ± 0.099 | 98000 ± 7800 | 37000 ± 2400 | 0.3 ± 0.0095 | -0.52 ± 0.014 | 9.1 ± 0.1 | 10 ± 0.072 |
4 Results
4.1 Cortical Folding Model
summary(lm(
1 / 2 * log10(AvgThickness) + log10(TotalArea) ~ log10(ExposedArea),
data = dados_hemi_v1,
na.action = na.omit
))##
## Call:
## lm(formula = 1/2 * log10(AvgThickness) + log10(TotalArea) ~ log10(ExposedArea),
## data = dados_hemi_v1, na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.044853 -0.009505 0.000683 0.010895 0.035787
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.01789 0.14975 0.119 0.905
## log10(ExposedArea) 1.13042 0.03275 34.511 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01519 on 244 degrees of freedom
## Multiple R-squared: 0.83, Adjusted R-squared: 0.8293
## F-statistic: 1191 on 1 and 244 DF, p-value: < 2.2e-16tidy(lm(
1 / 2 * log10(AvgThickness) + log10(TotalArea) ~ log10(ExposedArea),
data = dados_hemi_v1,
na.action = na.omit
), conf.int = TRUE)## # A tibble: 2 × 7
## term estimate std.error statistic p.value conf.low conf.high
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 (Intercept) 0.0179 0.150 0.119 9.05e- 1 -0.277 0.313
## 2 log10(ExposedArea) 1.13 0.0328 34.5 7.46e-96 1.07 1.19paste(
"Student's t test comapring slope with theoretical value 1.25. t = ",
signif(abs(coef(summary(
lm(
1 / 2 * log10(AvgThickness) + log10(TotalArea) ~ log10(ExposedArea),
data = dados_hemi_v1,
na.action = na.omit
)
))[2, 1] - 1.25) / coef(summary(
lm(
1 / 2 * log10(AvgThickness) + log10(TotalArea) ~ log10(ExposedArea),
data = dados_hemi_v1,
na.action = na.omit
)
))[2, 2], 3)
)## [1] "Student's t test comapring slope with theoretical value 1.25. t = 3.65"paste(
"Student's t test comapring slope with theoretical value 1.25. p value = ",
signif(test_coef(
lm(
1 / 2 * log10(AvgThickness) + log10(TotalArea) ~ log10(ExposedArea),
data = dados_hemi_v1,
na.action = na.omit
),
2,
1.25
),3)
)## [1] "Student's t test comapring slope with theoretical value 1.25. p value = 0.00032"5 Figure 1 A
fig2a <- ggplot(dados_hemi_v1,
aes(
log10(ExposedArea),
log10(sqrt(AvgThickness) * TotalArea),
color = Diagnostic,
fill = Diagnostic,
alpha = 0.4
)) +
geom_point() +
geom_smooth(method = "lm", se = TRUE) +
geom_abline(slope = 1.25,
intercept = coef(lm(
log10(sqrt(AvgThickness) * TotalArea) ~ log10(ExposedArea),
data = dados_hemi_v1,
na.action = na.omit
))[1],
color = "black") +
labs(x = expression('log'[10] * 'A'['E']),
y = expression('log'[10] * 'A'['T'] * sqrt('T'))) +
guides(alpha = "none") +
theme_pubr() +
scale_x_continuous(limits = c(4.45, 4.65)) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)lm_fit_comp_idor <- dados_hemi_v1 %>%
group_by(Diagnostic) %>%
do(fit_comp_idor = glance(lm(log10(sqrt(AvgThickness)*TotalArea) ~ log10(ExposedArea), data = ., na.action = na.omit), conf.int = TRUE)) %>% unnest()
lm_fit_comp_idor %>%
kable(digits = 2) %>%
kable_styling()| Diagnostic | r.squared | adj.r.squared | sigma | statistic | p.value | df | logLik | AIC | BIC | deviance | df.residual | nobs |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AD | 0.87 | 0.86 | 0.01 | 158.83 | 0 | 1 | 73.85 | -141.71 | -137.93 | 0.01 | 24 | 26 |
| MCI | 0.88 | 0.88 | 0.01 | 487.51 | 0 | 1 | 199.00 | -391.99 | -385.42 | 0.01 | 64 | 66 |
| CTL | 0.85 | 0.85 | 0.01 | 871.32 | 0 | 1 | 442.46 | -878.92 | -869.81 | 0.03 | 152 | 154 |
lm_fit_comp_idor <- dados_hemi_v1 %>%
group_by(Diagnostic) %>%
do(fit_comp_idor = tidy(lm(log10(sqrt(AvgThickness)*TotalArea) ~ log10(ExposedArea), data = ., na.action = na.omit), conf.int = TRUE)) %>% unnest()
lm_fit_comp_idor %>%
kable(digits = 2) %>%
kable_styling()| Diagnostic | term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|
| AD | (Intercept) | 0.20 | 0.39 | 0.50 | 0.62 | -0.62 | 1.01 |
| AD | log10(ExposedArea) | 1.09 | 0.09 | 12.60 | 0.00 | 0.91 | 1.27 |
| MCI | (Intercept) | 0.53 | 0.21 | 2.50 | 0.02 | 0.11 | 0.95 |
| MCI | log10(ExposedArea) | 1.02 | 0.05 | 22.08 | 0.00 | 0.93 | 1.11 |
| CTL | (Intercept) | -0.23 | 0.18 | -1.27 | 0.21 | -0.60 | 0.13 |
| CTL | log10(ExposedArea) | 1.19 | 0.04 | 29.52 | 0.00 | 1.11 | 1.27 |
N_subj_diag <- dados_hemi_v1 %>%
group_by(Diagnostic) %>%
summarise(N_SUBJ = n_distinct(SUBJ))
ggplot(
data = filter(lm_fit_comp_idor, term == "log10(ExposedArea)"),
aes(x = Diagnostic,
y = estimate, color = Diagnostic)
) +
geom_point() +
geom_errorbar(aes(ymin = estimate - std.error, ymax = estimate + std.error)) +
geom_hline(yintercept = 1.25, linetype = "dashed") +
theme_pubr() +
labs(y = "Slope") +
theme(legend.position = "none"
) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
5.1 Correlation within cortical fodling variables and age healthy subjects)
5.1.1 Slope alpha (SI Appendix, Text SI)
lm_Age <-
filter(
dados_hemi_v1,
Diagnostic == "CTL",
Age_interval != "[40,45)",
Age_interval != "[80,85)"
) %>%
group_by(Age_interval) %>%
do(fit_Age = tidy(
lm(
1 / 2 * log10(AvgThickness) + log10(TotalArea) ~ log10(ExposedArea),
data = .,
na.action = na.omit
),
conf.int = TRUE
)) %>%
unnest(cols = c(fit_Age))
N_subj <- filter(dados_hemi_v1, Diagnostic == "CTL", Age_interval != "[40,45)", Age_interval != "[80,85)") %>%
group_by(Age_interval) %>%
summarise(N_SUBJ = n_distinct(SUBJ))
fig1a <- ggplot(
data = filter(lm_Age, term == "log10(ExposedArea)"),
aes(
x = Age_interval,
y = estimate
)
) +
geom_point() +
geom_errorbar(aes(ymin = estimate - std.error, ymax = estimate + std.error)) +
geom_smooth(method = "lm", se = TRUE) +
geom_hline(yintercept = 1.25, linetype = "dashed") +
geom_text(aes(label = N_subj$N_SUBJ), nudge_y = 0.6) +
theme_pubr() +
labs(y = "Slope", x = "Age [years]") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)lm_Age <- lm_Age %>% mutate(Age_interval = as.double((str_sub(Age_interval,2,3))))
cor.test(filter(lm_Age, term == "log10(ExposedArea)")$estimate, filter(lm_Age, term == "log10(ExposedArea)")$Age_interval)##
## Pearson's product-moment correlation
##
## data: filter(lm_Age, term == "log10(ExposedArea)")$estimate and filter(lm_Age, term == "log10(ExposedArea)")$Age_interval
## t = -2.8822, df = 5, p-value = 0.0345
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.96750265 -0.09146064
## sample estimates:
## cor
## -0.79010045.1.2 K (Figure 1 B)
fig1b <- ggplot(data = dados_hemi_v1, aes(Age, K, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
geom_point() +
geom_smooth(method = "lm", se = TRUE) +
theme_pubr() +
guides(alpha = "none", color = "none", fill = "none") +
labs(x = "Age [years]") +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
fig1b
cor.test(filter(dados_hemi_v1, Diagnostic == "CTL")$K, filter(dados_hemi_v1, Diagnostic == "CTL")$Age)##
## Pearson's product-moment correlation
##
## data: filter(dados_hemi_v1, Diagnostic == "CTL")$K and filter(dados_hemi_v1, Diagnostic == "CTL")$Age
## t = -4.176, df = 152, p-value = 4.981e-05
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.4558437 -0.1713459
## sample estimates:
## cor
## -0.32081255.1.3 S
ggplot(data = dados_hemi_v1, aes(Age, S, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
geom_point() +
geom_smooth(method = "lm", se = TRUE) +
theme_pubr() +
guides(alpha = "none", color = "none", fill = "none") +
labs(x = "Age [years]") +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
cor.test(filter(dados_hemi_v1, Diagnostic == "CTL")$S, filter(dados_hemi_v1, Diagnostic == "CTL")$Age)##
## Pearson's product-moment correlation
##
## data: filter(dados_hemi_v1, Diagnostic == "CTL")$S and filter(dados_hemi_v1, Diagnostic == "CTL")$Age
## t = 1.546, df = 152, p-value = 0.1242
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.03441253 0.27713228
## sample estimates:
## cor
## 0.12442545.1.4 I
ggplot(data = dados_hemi_v1, aes(Age, I, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
geom_point() +
geom_smooth(method = "lm", se = TRUE) +
theme_pubr() +
guides(alpha = "none", color = "none", fill = "none") +
labs(x = "Age [years]") +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
cor.test(filter(dados_hemi_v1, Diagnostic == "CTL")$I, filter(dados_hemi_v1, Diagnostic == "CTL")$Age)##
## Pearson's product-moment correlation
##
## data: filter(dados_hemi_v1, Diagnostic == "CTL")$I and filter(dados_hemi_v1, Diagnostic == "CTL")$Age
## t = -6.6178, df = 152, p-value = 5.879e-10
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.5871863 -0.3402379
## sample estimates:
## cor
## -0.47294826 Figure 1
fig1_alt_2 <-
ggarrange(
fig2a,
fig1b,
labels = c("A", "B"),
ncol = 1,
common.legend = TRUE,
legend = "bottom"
)
fig1_alt_2
Age and diagnostic effects in cortical gyrification. We included 77 CTL (blue), 33 MCI (green) and 13 AD (red) subjects (A) Linear fitting for the model variables in each Diagnostic group, CTL (adjusted R²=0.85, and CTL (adjusted R²=0.097, MCI (adjusted R²=0.044, p=0.0051), p<0.0001), and AD (adjusted R²=0.86, p<0.0001), MCI (adjusted R²=0.88, p<0.0001). As the severity of the disease increase, the linear tendency is downshifted, with smaller linear intercepts (K). (B) K linear tendency with age with its 95% CI for the three diagnostics groups: AD (adjusted R²=0.026
ggsave("fig1_alt_2.pdf", plot = fig1_alt_2, dpi=1200, width = 8.7, height = 11, units = "cm", device = "pdf")6.1 Diagnostic discrimination and prediction.
6.1.1 K difference
aov <- aov(K ~ Diagnostic, data = dados_hemi_v1)
summary(aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01121 0.005607 28.27 9.13e-12 ***
## Residuals 243 0.04819 0.000198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic, data = dados_hemi_v1)
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.015650636 0.007961177 0.02334010 0.0000083
## CTL-AD 0.021969964 0.014928714 0.02901121 0.0000000
## CTL-MCI 0.006319328 0.001433485 0.01120517 0.00714866.1.2 K is reduced with age, as cortical thickness, total area and exposed area
K decrease with age is shown on Figure 1 B. Cortical Thickness, Total area and Exposed area:
T <- ggplot(data = dados_hemi_v1, aes(Age, AvgThickness, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
geom_point() +
geom_smooth(method = "lm", se = TRUE) +
theme_pubr() +
guides(alpha = "none", color = "none", fill = "none") +
labs(x = "Age [years]") +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
AT <- ggplot(data = dados_hemi_v1, aes(Age, TotalArea, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
geom_point() +
geom_smooth(method = "lm", se = TRUE) +
theme_pubr() +
guides(alpha = "none", color = "none", fill = "none") +
labs(x = "Age [years]", y = "Total Area 10^-5 ") +
scale_y_continuous(
labels = function(x)
x / 10000) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
AE <- ggplot(data = dados_hemi_v1, aes(Age, ExposedArea, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
geom_point() +
geom_smooth(method = "lm", se = TRUE) +
theme_pubr() +
guides(alpha = "none", color = "none", fill = "none") +
labs(x = "Age [years]", y = "Exposed Area 10^-5 ") +
scale_y_continuous(
labels = function(x)
x / 10000) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
ggarrange(T, AT, AE, ncol = 1, common.legend = TRUE, legend = "bottom")
6.1.3 Lobes diagnostic discrimination
6.1.3.1 Frontal Lobe
aov <- aov(K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "F"))
summary(aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00904 0.004519 14.71 9.49e-07 ***
## Residuals 239 0.07345 0.000307
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "F"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.014609191 0.0050152217 0.02420316 0.0011588
## CTL-AD 0.019888810 0.0111100645 0.02866756 0.0000006
## CTL-MCI 0.005279619 -0.0008538009 0.01141304 0.10724246.1.3.2 Occipital Lobe
aov <- aov(K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "O"))
summary(aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01088 0.005439 10.07 6.3e-05 ***
## Residuals 239 0.12902 0.000540
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "O"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.014822783 0.002107549 0.02753802 0.0176045
## CTL-AD 0.021529149 0.009894360 0.03316394 0.0000563
## CTL-MCI 0.006706366 -0.001422478 0.01483521 0.12825896.1.3.3 Parietal Lobe
aov <- aov(K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "P"))
summary(aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01254 0.006269 16.65 1.7e-07 ***
## Residuals 239 0.08998 0.000376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "P"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.021557695 0.010938941 0.032176449 0.0000088
## CTL-AD 0.023693603 0.013977151 0.033410055 0.0000001
## CTL-MCI 0.002135908 -0.004652656 0.008924473 0.73872266.1.3.4 Temporal Lobe
aov <- aov(K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "T"))
summary(aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01975 0.009876 28.49 7.97e-12 ***
## Residuals 239 0.08284 0.000347
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aov)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "T"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.01547690 0.005288318 0.02566549 0.0011957
## CTL-AD 0.02747185 0.018149018 0.03679469 0.0000000
## CTL-MCI 0.01199495 0.005481393 0.01850851 0.00006157 Figure S1
# K ----
dados_hemi_v1 %>%
group_by(Diagnostic) %>%
summarise(N_SUBJ = n_distinct(SUBJ))## # A tibble: 3 × 2
## Diagnostic N_SUBJ
## <fct> <int>
## 1 AD 13
## 2 MCI 33
## 3 CTL 77dados_lobos_v1 %>%
group_by(Diagnostic) %>%
summarise(N_SUBJ = n_distinct(SUBJ))## # A tibble: 3 × 2
## Diagnostic N_SUBJ
## <fct> <int>
## 1 AD 13
## 2 MCI 33
## 3 CTL 77a <- aov(K ~ Diagnostic, data = dados_hemi_v1)
summary(a)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01121 0.005607 28.27 9.13e-12 ***
## Residuals 243 0.04819 0.000198
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(a)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic, data = dados_hemi_v1)
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.015650636 0.007961177 0.02334010 0.0000083
## CTL-AD 0.021969964 0.014928714 0.02901121 0.0000000
## CTL-MCI 0.006319328 0.001433485 0.01120517 0.0071486a.TukeyHSD <- as.data.frame(TukeyHSD(a)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a)$`Diagnostic`))
a.TukeyHSD <- as_tibble(cbind(Contrasts, a.TukeyHSD))
rownames(a.TukeyHSD) <- NULL
colnames(a.TukeyHSD)[1] <- c("Contrast")
a.TukeyHSD <- a.TukeyHSD %>% mutate(ROI = "Hemisphere", variable = "K", agecorrection = "no")
a.F <- aov(K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "F"))
summary(a.F)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00904 0.004519 14.71 9.49e-07 ***
## Residuals 239 0.07345 0.000307
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(a.F)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "F"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.014609191 0.0050152217 0.02420316 0.0011588
## CTL-AD 0.019888810 0.0111100645 0.02866756 0.0000006
## CTL-MCI 0.005279619 -0.0008538009 0.01141304 0.1072424a.F.TukeyHSD <- as.data.frame(TukeyHSD(a.F)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.F)$`Diagnostic`))
a.F.TukeyHSD <- as_tibble(cbind(Contrasts, a.F.TukeyHSD))
rownames(a.F.TukeyHSD) <- NULL
colnames(a.F.TukeyHSD)[1] <- c("Contrast")
a.F.TukeyHSD <- a.F.TukeyHSD %>% mutate(ROI = "Frontal Lobe", variable = "K", agecorrection = "no")
a.O <- aov(K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "O"))
summary(a.O)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01088 0.005439 10.07 6.3e-05 ***
## Residuals 239 0.12902 0.000540
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(a.O)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "O"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.014822783 0.002107549 0.02753802 0.0176045
## CTL-AD 0.021529149 0.009894360 0.03316394 0.0000563
## CTL-MCI 0.006706366 -0.001422478 0.01483521 0.1282589a.O.TukeyHSD <- as.data.frame(TukeyHSD(a.O)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.O)$`Diagnostic`))
a.O.TukeyHSD <- as_tibble(cbind(Contrasts, a.O.TukeyHSD))
rownames(a.O.TukeyHSD) <- NULL
colnames(a.O.TukeyHSD)[1] <- c("Contrast")
a.O.TukeyHSD <- a.O.TukeyHSD %>% mutate(ROI = "Occipital Lobe", variable = "K", agecorrection = "no")
a.P <- aov(K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "P"))
summary(a.P)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01254 0.006269 16.65 1.7e-07 ***
## Residuals 239 0.08998 0.000376
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(a.P)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "P"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.021557695 0.010938941 0.032176449 0.0000088
## CTL-AD 0.023693603 0.013977151 0.033410055 0.0000001
## CTL-MCI 0.002135908 -0.004652656 0.008924473 0.7387226a.P.TukeyHSD <- as.data.frame(TukeyHSD(a.P)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.P)$`Diagnostic`))
a.P.TukeyHSD <- as_tibble(cbind(Contrasts, a.P.TukeyHSD))
rownames(a.P.TukeyHSD) <- NULL
colnames(a.P.TukeyHSD)[1] <- c("Contrast")
a.P.TukeyHSD <- a.P.TukeyHSD %>% mutate(ROI = "Parietal Lobe", variable = "K", agecorrection = "no")
a.T <- aov(K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "T"))
summary(a.T)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01975 0.009876 28.49 7.97e-12 ***
## Residuals 239 0.08284 0.000347
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(a.T)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "T"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.01547690 0.005288318 0.02566549 0.0011957
## CTL-AD 0.02747185 0.018149018 0.03679469 0.0000000
## CTL-MCI 0.01199495 0.005481393 0.01850851 0.0000615a.T.TukeyHSD <- as.data.frame(TukeyHSD(a.T)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.T)$`Diagnostic`))
a.T.TukeyHSD <- as_tibble(cbind(Contrasts, a.T.TukeyHSD))
rownames(a.T.TukeyHSD) <- NULL
colnames(a.T.TukeyHSD)[1] <- c("Contrast")
a.T.TukeyHSD <- a.T.TukeyHSD %>% mutate(ROI = "Temporal Lobe", variable = "K", agecorrection = "no")
aov_summary <- full_join(a.TukeyHSD, a.F.TukeyHSD) %>% full_join(a.O.TukeyHSD) %>% full_join(a.P.TukeyHSD) %>% full_join(a.T.TukeyHSD)
# K age decay ----
a <- aov(K_age_decay ~ Diagnostic, data = dados_hemi_v1)
summary(a)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00478 0.0023923 15.69 3.91e-07 ***
## Residuals 243 0.03706 0.0001525
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(a)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K_age_decay ~ Diagnostic, data = dados_hemi_v1)
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.011591895 0.004849210 0.018334579 0.0001997
## CTL-AD 0.014605421 0.008431134 0.020779708 0.0000002
## CTL-MCI 0.003013526 -0.001270742 0.007297794 0.2233236a.TukeyHSD <- as.data.frame(TukeyHSD(a)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a)$`Diagnostic`))
a.TukeyHSD <- as_tibble(cbind(Contrasts, a.TukeyHSD))
rownames(a.TukeyHSD) <- NULL
colnames(a.TukeyHSD)[1] <- c("Contrast")
a.TukeyHSD <- a.TukeyHSD %>% mutate(ROI = "Hemisphere", variable = "K", agecorrection = "yes")
a.F <- aov(K_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "F"))
summary(a.F)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00361 0.0018049 7.345 0.000802 ***
## Residuals 239 0.05873 0.0002457
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(a.F)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "F"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.010592555 0.002013654 0.019171456 0.0109327
## CTL-AD 0.012750436 0.004900506 0.020600366 0.0004781
## CTL-MCI 0.002157881 -0.003326606 0.007642368 0.6231806a.F.TukeyHSD <- as.data.frame(TukeyHSD(a.F)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.F)$`Diagnostic`))
a.F.TukeyHSD <- as_tibble(cbind(Contrasts, a.F.TukeyHSD))
rownames(a.F.TukeyHSD) <- NULL
colnames(a.F.TukeyHSD)[1] <- c("Contrast")
a.F.TukeyHSD <- a.F.TukeyHSD %>% mutate(ROI = "Frontal Lobe", variable = "K", agecorrection = "yes")
a.O <- aov(K_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "O"))
summary(a.O)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00575 0.0028763 6.521 0.00175 **
## Residuals 239 0.10542 0.0004411
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(a.O)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "O"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.011558848 6.491843e-05 0.02305278 0.0483683
## CTL-AD 0.015846744 5.329482e-03 0.02636401 0.0013265
## CTL-MCI 0.004287895 -3.060169e-03 0.01163596 0.3549990a.O.TukeyHSD <- as.data.frame(TukeyHSD(a.O)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.O)$`Diagnostic`))
a.O.TukeyHSD <- as_tibble(cbind(Contrasts, a.O.TukeyHSD))
rownames(a.O.TukeyHSD) <- NULL
colnames(a.O.TukeyHSD)[1] <- c("Contrast")
a.O.TukeyHSD <- a.O.TukeyHSD %>% mutate(ROI = "Occipital Lobe", variable = "K", agecorrection = "yes")
a.P <- aov(K_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "P"))
summary(a.P)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00604 0.003020 10.23 5.43e-05 ***
## Residuals 239 0.07051 0.000295
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(a.P)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "P"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.0167424548 0.007342507 0.026142402 0.0001111
## CTL-AD 0.0157902985 0.007189088 0.024391509 0.0000651
## CTL-MCI -0.0009521562 -0.006961538 0.005057226 0.9259479a.P.TukeyHSD <- as.data.frame(TukeyHSD(a.P)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.P)$`Diagnostic`))
a.P.TukeyHSD <- as_tibble(cbind(Contrasts, a.P.TukeyHSD))
rownames(a.P.TukeyHSD) <- NULL
colnames(a.P.TukeyHSD)[1] <- c("Contrast")
a.P.TukeyHSD <- a.P.TukeyHSD %>% mutate(ROI = "Parietal Lobe", variable = "K", agecorrection = "yes")
a.T <- aov(K_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "T"))
summary(a.T)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00681 0.003405 13.31 3.31e-06 ***
## Residuals 239 0.06114 0.000256
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(a.T)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "T"))
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.009981324 0.0012282029 0.01873445 0.0208566
## CTL-AD 0.016475471 0.0084661238 0.02448482 0.0000066
## CTL-MCI 0.006494146 0.0008982799 0.01209001 0.0182332a.T.TukeyHSD <- as.data.frame(TukeyHSD(a.T)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.T)$`Diagnostic`))
a.T.TukeyHSD <- as_tibble(cbind(Contrasts, a.T.TukeyHSD))
rownames(a.T.TukeyHSD) <- NULL
colnames(a.T.TukeyHSD)[1] <- c("Contrast")
a.T.TukeyHSD <- a.T.TukeyHSD %>% mutate(ROI = "Temporal Lobe", variable = "K", agecorrection = "yes")
aov_summary <- full_join(aov_summary, a.TukeyHSD) %>% full_join(a.F.TukeyHSD) %>% full_join(a.O.TukeyHSD) %>% full_join(a.P.TukeyHSD) %>% full_join(a.T.TukeyHSD)
# logAvgThickness ----
a <- aov(logAvgThickness ~ Diagnostic, data = dados_hemi_v1)
summary(a)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01343 0.006717 25.3 1.05e-10 ***
## Residuals 243 0.06452 0.000265
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1a.TukeyHSD <- as.data.frame(TukeyHSD(a)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a)$`Diagnostic`))
a.TukeyHSD <- as_tibble(cbind(Contrasts, a.TukeyHSD))
rownames(a.TukeyHSD) <- NULL
colnames(a.TukeyHSD)[1] <- c("Contrast")
a.TukeyHSD <- a.TukeyHSD %>% mutate(ROI = "Hemisphere", variable = "log[10]T", agecorrection = "no")
a.F <- aov(logAvgThickness ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "F"))
summary(a.F)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01143 0.005714 16.86 1.41e-07 ***
## Residuals 239 0.08098 0.000339
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1a.F.TukeyHSD <- as.data.frame(TukeyHSD(a.F)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.F)$`Diagnostic`))
a.F.TukeyHSD <- as_tibble(cbind(Contrasts, a.F.TukeyHSD))
rownames(a.F.TukeyHSD) <- NULL
colnames(a.F.TukeyHSD)[1] <- c("Contrast")
a.F.TukeyHSD <- a.F.TukeyHSD %>% mutate(ROI = "Frontal Lobe", variable = "log[10]T", agecorrection = "no")
a.O <- aov(logAvgThickness ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "O"))
summary(a.O)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.0043 0.002149 5.142 0.00651 **
## Residuals 239 0.0999 0.000418
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1a.O.TukeyHSD <- as.data.frame(TukeyHSD(a.O)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.O)$`Diagnostic`))
a.O.TukeyHSD <- as_tibble(cbind(Contrasts, a.O.TukeyHSD))
rownames(a.O.TukeyHSD) <- NULL
colnames(a.O.TukeyHSD)[1] <- c("Contrast")
a.O.TukeyHSD <- a.O.TukeyHSD %>% mutate(ROI = "Occipital Lobe", variable = "log[10]T", agecorrection = "no")
a.P <- aov(logAvgThickness ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "P"))
summary(a.P)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00769 0.003843 11.8 1.3e-05 ***
## Residuals 239 0.07786 0.000326
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1a.P.TukeyHSD <- as.data.frame(TukeyHSD(a.P)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.P)$`Diagnostic`))
a.P.TukeyHSD <- as_tibble(cbind(Contrasts, a.P.TukeyHSD))
rownames(a.P.TukeyHSD) <- NULL
colnames(a.P.TukeyHSD)[1] <- c("Contrast")
a.P.TukeyHSD <- a.P.TukeyHSD %>% mutate(ROI = "Parietal Lobe", variable = "log[10]T", agecorrection = "no")
a.T <- aov(logAvgThickness ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "T"))
summary(a.T)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.03180 0.015898 41.54 3.28e-16 ***
## Residuals 239 0.09146 0.000383
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1a.T.TukeyHSD <- as.data.frame(TukeyHSD(a.T)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.T)$`Diagnostic`))
a.T.TukeyHSD <- as_tibble(cbind(Contrasts, a.T.TukeyHSD))
rownames(a.T.TukeyHSD) <- NULL
colnames(a.T.TukeyHSD)[1] <- c("Contrast")
a.T.TukeyHSD <- a.T.TukeyHSD %>% mutate(ROI = "Temporal Lobe", variable = "log[10]T", agecorrection = "no")
aov_summary <- full_join(aov_summary, a.TukeyHSD) %>% full_join(a.F.TukeyHSD) %>% full_join(a.O.TukeyHSD) %>% full_join(a.P.TukeyHSD) %>% full_join(a.T.TukeyHSD)
# logAvgThickness age decay ----
a <- aov(logAvgThickness_age_decay ~ Diagnostic, data = dados_hemi_v1)
summary(a)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00333 0.0016661 9.025 0.000166 ***
## Residuals 243 0.04486 0.0001846
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1a.TukeyHSD <- as.data.frame(TukeyHSD(a)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a)$`Diagnostic`))
a.TukeyHSD <- as_tibble(cbind(Contrasts, a.TukeyHSD))
rownames(a.TukeyHSD) <- NULL
colnames(a.TukeyHSD)[1] <- c("Contrast")
a.TukeyHSD <- a.TukeyHSD %>% mutate(ROI = "Hemisphere", variable = "log[10]T", agecorrection = "yes")
a.F <- aov(logAvgThickness_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "F"))
summary(a.F)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00259 0.0012967 5.359 0.00529 **
## Residuals 239 0.05782 0.0002419
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1a.F.TukeyHSD <- as.data.frame(TukeyHSD(a.F)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.F)$`Diagnostic`))
a.F.TukeyHSD <- as_tibble(cbind(Contrasts, a.F.TukeyHSD))
rownames(a.F.TukeyHSD) <- NULL
colnames(a.F.TukeyHSD)[1] <- c("Contrast")
a.F.TukeyHSD <- a.F.TukeyHSD %>% mutate(ROI = "Frontal Lobe", variable = "log[10]T", agecorrection = "yes")
a.O <- aov(logAvgThickness_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "O"))
summary(a.O)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00095 0.0004756 1.316 0.27
## Residuals 239 0.08639 0.0003615a.O.TukeyHSD <- as.data.frame(TukeyHSD(a.O)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.O)$`Diagnostic`))
a.O.TukeyHSD <- as_tibble(cbind(Contrasts, a.O.TukeyHSD))
rownames(a.O.TukeyHSD) <- NULL
colnames(a.O.TukeyHSD)[1] <- c("Contrast")
a.O.TukeyHSD <- a.O.TukeyHSD %>% mutate(ROI = "Occipital Lobe", variable = "log[10]T", agecorrection = "yes")
a.P <- aov(logAvgThickness_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "P"))
summary(a.P)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00089 0.0004448 2.067 0.129
## Residuals 239 0.05144 0.0002152a.P.TukeyHSD <- as.data.frame(TukeyHSD(a.P)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.P)$`Diagnostic`))
a.P.TukeyHSD <- as_tibble(cbind(Contrasts, a.P.TukeyHSD))
rownames(a.P.TukeyHSD) <- NULL
colnames(a.P.TukeyHSD)[1] <- c("Contrast")
a.P.TukeyHSD <- a.P.TukeyHSD %>% mutate(ROI = "Parietal Lobe", variable = "log[10]T", agecorrection = "yes")
a.T <- aov(logAvgThickness_age_decay ~ Diagnostic, data = filter(dados_lobos_v1, ROI == "T"))
summary(a.T)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01274 0.006371 22.82 8.51e-10 ***
## Residuals 239 0.06672 0.000279
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1a.T.TukeyHSD <- as.data.frame(TukeyHSD(a.T)$`Diagnostic`)
Contrasts <- as.data.frame(rownames(TukeyHSD(a.T)$`Diagnostic`))
a.T.TukeyHSD <- as_tibble(cbind(Contrasts, a.T.TukeyHSD))
rownames(a.T.TukeyHSD) <- NULL
colnames(a.T.TukeyHSD)[1] <- c("Contrast")
a.T.TukeyHSD <- a.T.TukeyHSD %>% mutate(ROI = "Temporal Lobe", variable = "log[10]T", agecorrection = "yes")
# ----
aov_summary <- full_join(aov_summary, a.TukeyHSD) %>% full_join(a.F.TukeyHSD) %>% full_join(a.O.TukeyHSD) %>% full_join(a.P.TukeyHSD) %>% full_join(a.T.TukeyHSD)
agecorrection <- c(
"no" = "Raw data",
"yes" = "After age correction"
)
figs1 <- ggplot(data = filter(aov_summary, `p adj` < 0.05 | `p adj` == 0.05), aes(
x = reorder(ROI, desc(ROI)),
y = diff,
ymin = lwr,
ymax = upr, color = Contrast)) +
geom_hline(yintercept = 0,
linetype = "11",
colour = "grey60") +
geom_pointrange( position = position_dodge(width = 0.3)) +
# geom_text(aes(label = str_c("p adj = ", signif(`p adj`, digits = 2))), nudge_x = 0.3) +
coord_flip() +
labs(y = "Differences in mean levels of Diagnostic", x = "ROI") + facet_grid(variable ~ agecorrection, labeller = labeller(agecorrection = agecorrection)) +
theme_pubr() +
theme(axis.title = element_text(size = 11),
axis.text = element_text(size = 10), text = element_text(size = 10)) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
aov_summary[order(aov_summary$diff, decreasing = TRUE),]## # A tibble: 60 × 8
## Contrast diff lwr upr `p adj` ROI variable agecorrection
## <chr> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <chr>
## 1 CTL-AD 0.0358 0.0260 0.0456 4.41e-14 Temporal Lobe log[10]T no
## 2 CTL-AD 0.0275 0.0181 0.0368 1.04e-10 Temporal Lobe K no
## 3 CTL-AD 0.0237 0.0140 0.0334 8.08e- 8 Parietal Lobe K no
## 4 CTL-AD 0.0233 0.0151 0.0314 3.45e-10 Hemisphere log[10]T no
## 5 CTL-AD 0.0232 0.0149 0.0316 1.06e- 9 Temporal Lobe log[10]T yes
## 6 MCI-AD 0.0221 0.0114 0.0328 6.19e- 6 Temporal Lobe log[10]T no
## 7 CTL-AD 0.0220 0.0149 0.0290 8.55e-12 Hemisphere K no
## 8 CTL-AD 0.0217 0.0125 0.0309 2.18e- 7 Frontal Lobe log[10]T no
## 9 MCI-AD 0.0216 0.0109 0.0322 8.81e- 6 Parietal Lobe K no
## 10 CTL-AD 0.0215 0.00989 0.0332 5.63e- 5 Occipital Lobe K no
## # … with 50 more rowsfigs1
Statistically significant (p<0.05) differences in mean levels with the 95% Confidence Interval of Diagnostics for K and log(AvgThickness), with (After age correction) and without (Raw data) age correction for the hemisphere and the four lobes. Multiple corrections were applied within each morphological feature and ROI.
7.0.1 Difference in K between diagnostics decrease with age?
We compared age intervals of 10 years to increase N at each comparison.
Linear model for visual inspection:
b <- lm(K ~ Age * ROI * Diagnostic, data = dados)
summary(b)##
## Call:
## lm(formula = K ~ Age * ROI * Diagnostic, data = dados)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.062579 -0.012117 0.000376 0.012462 0.061381
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -4.175e-01 4.632e-02 -9.013 <2e-16 ***
## Age -9.139e-04 5.991e-04 -1.525 0.1274
## ROIhemisphere -7.977e-02 6.551e-02 -1.218 0.2236
## ROIO 7.811e-02 6.551e-02 1.192 0.2333
## ROIP 1.091e-01 6.551e-02 1.665 0.0962 .
## ROIT 7.845e-02 6.551e-02 1.198 0.2313
## DiagnosticMCI -2.951e-02 5.784e-02 -0.510 0.6100
## DiagnosticCTL -1.502e-02 4.784e-02 -0.314 0.7535
## Age:ROIhemisphere 2.715e-04 8.472e-04 0.320 0.7487
## Age:ROIO 4.846e-04 8.472e-04 0.572 0.5674
## Age:ROIP 5.873e-05 8.472e-04 0.069 0.9448
## Age:ROIT 2.245e-04 8.472e-04 0.265 0.7911
## Age:DiagnosticMCI 5.356e-04 7.671e-04 0.698 0.4852
## Age:DiagnosticCTL 3.757e-04 6.254e-04 0.601 0.5481
## ROIhemisphere:DiagnosticMCI 4.889e-02 8.225e-02 0.594 0.5524
## ROIO:DiagnosticMCI 3.163e-02 8.179e-02 0.387 0.6990
## ROIP:DiagnosticMCI 5.957e-02 8.179e-02 0.728 0.4666
## ROIT:DiagnosticMCI 1.041e-01 8.179e-02 1.273 0.2033
## ROIhemisphere:DiagnosticCTL 2.221e-02 6.761e-02 0.328 0.7426
## ROIO:DiagnosticCTL 3.178e-02 6.765e-02 0.470 0.6387
## ROIP:DiagnosticCTL 1.799e-02 6.765e-02 0.266 0.7904
## ROIT:DiagnosticCTL 4.475e-02 6.765e-02 0.662 0.5084
## Age:ROIhemisphere:DiagnosticMCI -6.302e-04 1.091e-03 -0.578 0.5637
## Age:ROIO:DiagnosticMCI -4.052e-04 1.085e-03 -0.374 0.7088
## Age:ROIP:DiagnosticMCI -7.229e-04 1.085e-03 -0.666 0.5053
## Age:ROIT:DiagnosticMCI -1.413e-03 1.085e-03 -1.302 0.1931
## Age:ROIhemisphere:DiagnosticCTL -2.606e-04 8.839e-04 -0.295 0.7681
## Age:ROIO:DiagnosticCTL -3.753e-04 8.845e-04 -0.424 0.6714
## Age:ROIP:DiagnosticCTL -2.050e-04 8.845e-04 -0.232 0.8168
## Age:ROIT:DiagnosticCTL -5.254e-04 8.845e-04 -0.594 0.5526
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01838 on 1192 degrees of freedom
## Multiple R-squared: 0.94, Adjusted R-squared: 0.9386
## F-statistic: 644.1 on 29 and 1192 DF, p-value: < 2.2e-16anova(b)## Analysis of Variance Table
##
## Response: K
## Df Sum Sq Mean Sq F value Pr(>F)
## Age 1 0.0591 0.05908 174.8143 < 2.2e-16 ***
## ROI 4 6.2246 1.55614 4604.2704 < 2.2e-16 ***
## Diagnostic 2 0.0231 0.01157 34.2401 3.477e-15 ***
## Age:ROI 4 0.0029 0.00073 2.1533 0.07222 .
## Age:Diagnostic 2 0.0003 0.00014 0.4199 0.65722
## ROI:Diagnostic 8 0.0019 0.00024 0.7152 0.67830
## Age:ROI:Diagnostic 8 0.0009 0.00011 0.3256 0.95649
## Residuals 1192 0.4029 0.00034
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1e <- allEffects(b)
e <- as.data.frame(e[[1]])
ggplot(e, aes(
x = Age,
y = fit,
color = Diagnostic,
ymin = lower,
ymax = upper
)) +
geom_pointrange() +
geom_line() +
theme_pubr() +
facet_wrap(ROI ~ .) +
labs(y = "K") +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
NSUBJ <-
dados %>% group_by(Diagnostic, ROI, Age_interval10) %>% summarise(N = n_distinct(SUBJ))
a <- aov(K ~ Diagnostic * ROI * Age_interval10, data = dados)
a## Call:
## aov(formula = K ~ Diagnostic * ROI * Age_interval10, data = dados)
##
## Terms:
## Diagnostic ROI Age_interval10 Diagnostic:ROI
## Sum of Squares 0.058398 6.222467 0.036026 0.002912
## Deg. of Freedom 2 4 4 8
## Diagnostic:Age_interval10 ROI:Age_interval10
## Sum of Squares 0.008345 0.004742
## Deg. of Freedom 4 16
## Diagnostic:ROI:Age_interval10 Residuals
## Sum of Squares 0.005051 0.377744
## Deg. of Freedom 16 1167
##
## Residual standard error: 0.01799133
## 20 out of 75 effects not estimable
## Estimated effects may be unbalancedsummary(a)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.058 0.0292 90.207 < 2e-16 ***
## ROI 4 6.222 1.5556 4805.912 < 2e-16 ***
## Age_interval10 4 0.036 0.0090 27.824 < 2e-16 ***
## Diagnostic:ROI 8 0.003 0.0004 1.125 0.344
## Diagnostic:Age_interval10 4 0.008 0.0021 6.446 3.95e-05 ***
## ROI:Age_interval10 16 0.005 0.0003 0.916 0.551
## Diagnostic:ROI:Age_interval10 16 0.005 0.0003 0.975 0.482
## Residuals 1167 0.378 0.0003
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1a.TukeyHSD <-
as.data.frame(TukeyHSD(a)$`Diagnostic:ROI:Age_interval10`)
Contrasts <-
as.data.frame(rownames(TukeyHSD(a)$`Diagnostic:ROI:Age_interval10`))
a.TukeyHSD <- as_tibble(cbind(Contrasts, a.TukeyHSD))
rownames(a.TukeyHSD) <- NULL
colnames(a.TukeyHSD)[1] <- c("Contrast")
a.TukeyHSD <- a.TukeyHSD %>%
mutate(
Contrast1 = str_split(a.TukeyHSD$Contrast, pattern = "-", simplify = TRUE)[, 1],
Contrast2 = str_split(a.TukeyHSD$Contrast, pattern = "-", simplify = TRUE)[, 2]
)
a.TukeyHSD <- a.TukeyHSD %>%
mutate(
Diagnostic.1 = str_split(
a.TukeyHSD$Contrast1,
pattern = ":",
simplify = TRUE
)[, 1],
Diagnostic.2 = str_split(
a.TukeyHSD$Contrast2,
pattern = ":",
simplify = TRUE
)[, 1],
ROI.1 = str_split(
a.TukeyHSD$Contrast1,
pattern = ":",
simplify = TRUE
)[, 2],
ROI.2 = str_split(
a.TukeyHSD$Contrast2,
pattern = ":",
simplify = TRUE
)[, 2],
Age.1 = str_split(
a.TukeyHSD$Contrast1,
pattern = ":",
simplify = TRUE
)[, 3],
Age.2 = str_split(
a.TukeyHSD$Contrast2,
pattern = ":",
simplify = TRUE
)[, 3],
Contrasts = str_c(Diagnostic.1, Diagnostic.2, sep = "-")
) %>% filter(ROI.1 == ROI.2 &
Age.1 == Age.2) %>% dplyr::select(-c(Contrast1 , Contrast2))
a.TukeyHSD$ROI.1 <- as.factor(a.TukeyHSD$ROI.1)
a.TukeyHSD$ROI.1 <- relevel(a.TukeyHSD$ROI.1, ref = "hemisphere")
figs2a <- ggplot(data = a.TukeyHSD, aes(
x = Age.1,
y = diff,
ymin = lwr,
ymax = upr,
color = ROI.1
)) +
geom_hline(yintercept = 0,
linetype = "11",
colour = "grey60") +
geom_pointrange() + geom_line(aes(group = ROI.1)) + facet_wrap(Contrasts ~ .) +
labs(y = "Differences in mean of K", x = "Age [years]", color = "ROI") +
theme_pubr() +
theme(
axis.title = element_text(size = 11),
axis.text = element_text(size = 10),
text = element_text(size = 10)
) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
a <- aov(K_age_decay ~ Diagnostic * ROI * Age_interval10, data = dados)
a## Call:
## aov(formula = K_age_decay ~ Diagnostic * ROI * Age_interval10,
## data = dados)
##
## Terms:
## Diagnostic ROI Age_interval10 Diagnostic:ROI
## Sum of Squares 0.023918 6.364823 0.009576 0.001663
## Deg. of Freedom 2 4 4 8
## Diagnostic:Age_interval10 ROI:Age_interval10
## Sum of Squares 0.006937 0.003000
## Deg. of Freedom 4 16
## Diagnostic:ROI:Age_interval10 Residuals
## Sum of Squares 0.004339 0.315550
## Deg. of Freedom 16 1167
##
## Residual standard error: 0.01644367
## 20 out of 75 effects not estimable
## Estimated effects may be unbalancedsummary(a)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.024 0.0120 44.228 < 2e-16 ***
## ROI 4 6.365 1.5912 5884.764 < 2e-16 ***
## Age_interval10 4 0.010 0.0024 8.854 4.83e-07 ***
## Diagnostic:ROI 8 0.002 0.0002 0.769 0.631
## Diagnostic:Age_interval10 4 0.007 0.0017 6.414 4.18e-05 ***
## ROI:Age_interval10 16 0.003 0.0002 0.693 0.803
## Diagnostic:ROI:Age_interval10 16 0.004 0.0003 1.003 0.451
## Residuals 1167 0.316 0.0003
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1a.TukeyHSD <-
as.data.frame(TukeyHSD(a)$`Diagnostic:ROI:Age_interval10`)
Contrasts <-
as.data.frame(rownames(TukeyHSD(a)$`Diagnostic:ROI:Age_interval10`))
a.TukeyHSD <- as_tibble(cbind(Contrasts, a.TukeyHSD))
rownames(a.TukeyHSD) <- NULL
colnames(a.TukeyHSD)[1] <- c("Contrast")
a.TukeyHSD <- a.TukeyHSD %>%
mutate(
Contrast1 = str_split(a.TukeyHSD$Contrast, pattern = "-", simplify = TRUE)[, 1],
Contrast2 = str_split(a.TukeyHSD$Contrast, pattern = "-", simplify = TRUE)[, 2]
)
a.TukeyHSD <- a.TukeyHSD %>%
mutate(
Diagnostic.1 = str_split(
a.TukeyHSD$Contrast1,
pattern = ":",
simplify = TRUE
)[, 1],
Diagnostic.2 = str_split(
a.TukeyHSD$Contrast2,
pattern = ":",
simplify = TRUE
)[, 1],
ROI.1 = str_split(
a.TukeyHSD$Contrast1,
pattern = ":",
simplify = TRUE
)[, 2],
ROI.2 = str_split(
a.TukeyHSD$Contrast2,
pattern = ":",
simplify = TRUE
)[, 2],
Age.1 = str_split(
a.TukeyHSD$Contrast1,
pattern = ":",
simplify = TRUE
)[, 3],
Age.2 = str_split(
a.TukeyHSD$Contrast2,
pattern = ":",
simplify = TRUE
)[, 3],
Contrasts = str_c(Diagnostic.1, Diagnostic.2, sep = "-")
) %>% filter(ROI.1 == ROI.2 &
Age.1 == Age.2) %>% dplyr::select(-c(Contrast1 , Contrast2))
a.TukeyHSD$ROI.1 <- as.factor(a.TukeyHSD$ROI.1)
a.TukeyHSD$ROI.1 <- relevel(a.TukeyHSD$ROI.1, ref = "hemisphere")
figs2b <- ggplot(data = a.TukeyHSD, aes(
x = Age.1,
y = diff,
ymin = lwr,
ymax = upr,
color = ROI.1
)) +
geom_hline(yintercept = 0,
linetype = "11",
colour = "grey60") +
geom_pointrange() + geom_line(aes(group = ROI.1)) + facet_wrap(Contrasts ~ .) +
labs(y = "Differences in mean of K (age corrected)", x = "Age [years]", color = "ROI") +
theme_pubr() +
theme(
axis.title = element_text(size = 11),
axis.text = element_text(size = 10),
text = element_text(size = 10)
) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
figs2 <-
ggarrange(
figs2a,
figs2b,
labels = c("A", "B"),
common.legend = TRUE,
legend = "top",
nrow = 2,
ncol = 1,
font.label = list(size = 11)
)
ggsave(
"figs2.pdf",
plot = figs2,
dpi = 1200,
width = 17.8,
height = 22,
units = "cm",
device = "pdf"
)8 Figure S2
figs2
Difference of means in pairwise comparison for AD-CTL, AD-MCI, and MCI-CTL in grouped by age in decades in each ROI for (A) K and (B) K after age correction. Bars represents 95% confidence interval. There is no statistical power, probably influenced by the small number of observations in each data point, to infer that the difference between diagnostics is more significant in younger adults.
8.0.1 Optimal cut-offs
## Method: maximize_boot_metric
## Predictor: K
## Outcome: Diagnostic
## Direction: <=
## Nr. of bootstraps: 1000
##
## AUC n n_pos n_neg
## 0.8442 180 26 154
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## -0.5402 1.4341 0.8167 0.5769 0.8571 15 11 22 132
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu.
## Overall -0.5705655 -0.5554250 -0.5369054 -0.5277599 -0.5279802 -0.5160266
## AD -0.5705655 -0.5675134 -0.5595947 -0.5523513 -0.5467767 -0.5315364
## MCI NA NA NA NA NaN NA
## CTL -0.5553652 -0.5484493 -0.5346425 -0.5249779 -0.5248068 -0.5145646
## 95% Max. SD NAs
## -0.5032479 -0.4974075 0.01602744 0
## -0.5260303 -0.5225081 0.01543894 0
## NA NA NA 0
## -0.5028530 -0.4974075 0.01383501 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## optimal_cutpoint -0.55 -0.55 -0.54 -0.54 -0.54 -0.53 -0.53 -0.52 0.01
## AUC_b 0.67 0.78 0.82 0.85 0.84 0.87 0.90 0.96 0.04
## AUC_oob 0.60 0.74 0.81 0.85 0.84 0.88 0.93 1.00 0.06
## sum_sens_spec_b 1.12 1.26 1.37 1.46 1.46 1.55 1.67 1.85 0.12
## sum_sens_spec_oob 0.85 1.19 1.33 1.42 1.41 1.50 1.62 1.81 0.13
## acc_b 0.48 0.65 0.72 0.78 0.78 0.85 0.92 0.96 0.08
## acc_oob 0.49 0.62 0.71 0.78 0.77 0.84 0.90 0.97 0.09
## sensitivity_b 0.40 0.50 0.58 0.63 0.66 0.71 0.88 1.00 0.11
## sensitivity_oob 0.00 0.33 0.50 0.60 0.62 0.75 0.93 1.00 0.19
## specificity_b 0.43 0.63 0.73 0.81 0.80 0.89 0.95 0.99 0.10
## specificity_oob 0.36 0.58 0.71 0.81 0.79 0.89 0.96 1.00 0.12
## cohens_kappa_b 0.08 0.16 0.25 0.33 0.36 0.47 0.65 0.83 0.15
## cohens_kappa_oob -0.08 0.13 0.23 0.31 0.32 0.39 0.53 0.78 0.12
## NAs
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## Method: maximize_boot_metric
## Predictor: K
## Outcome: Diagnostic
## Direction: <=
## Nr. of bootstraps: 1000
##
## AUC n n_pos n_neg
## 0.6232 220 66 154
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## -0.5293 1.1753 0.6045 0.5455 0.6299 36 30 57 97
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu.
## Overall -0.5680537 -0.5499131 -0.5369054 -0.5269576 -0.5267026 -0.5160860
## AD NA NA NA NA NaN NA
## MCI -0.5680537 -0.5526269 -0.5402736 -0.5314849 -0.5311261 -0.5215015
## CTL -0.5553652 -0.5484493 -0.5346425 -0.5249779 -0.5248068 -0.5145646
## 95% Max. SD NAs
## -0.5046477 -0.4974075 0.01418700 0
## NA NA NA 0
## -0.5081453 -0.5055914 0.01411384 0
## -0.5028530 -0.4974075 0.01383501 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## optimal_cutpoint -0.54 -0.54 -0.53 -0.53 -0.53 -0.52 -0.52 -0.51 0.00
## AUC_b 0.49 0.56 0.60 0.62 0.62 0.65 0.69 0.78 0.04
## AUC_oob 0.42 0.53 0.58 0.62 0.62 0.66 0.71 0.80 0.06
## sum_sens_spec_b 0.95 1.06 1.13 1.19 1.18 1.23 1.31 1.50 0.07
## sum_sens_spec_oob 0.78 0.98 1.08 1.14 1.14 1.21 1.30 1.48 0.10
## acc_b 0.40 0.50 0.55 0.60 0.59 0.64 0.69 0.77 0.06
## acc_oob 0.39 0.47 0.53 0.58 0.58 0.62 0.68 0.76 0.07
## sensitivity_b 0.30 0.42 0.52 0.57 0.59 0.65 0.78 0.89 0.11
## sensitivity_oob 0.13 0.32 0.46 0.56 0.56 0.67 0.80 1.00 0.15
## specificity_b 0.23 0.41 0.50 0.61 0.60 0.69 0.78 0.88 0.12
## specificity_oob 0.21 0.37 0.48 0.59 0.58 0.68 0.78 0.92 0.13
## cohens_kappa_b -0.04 0.05 0.11 0.16 0.16 0.21 0.28 0.49 0.07
## cohens_kappa_oob -0.15 -0.02 0.07 0.12 0.12 0.18 0.27 0.44 0.09
## NAs
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## Method: maximize_boot_metric
## Predictor: logAvgThickness
## Outcome: Diagnostic
## Direction: <=
## Nr. of bootstraps: 1000
##
## AUC n n_pos n_neg
## 0.8492 180 26 154
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## 0.3877 1.4965 0.7333 0.7692 0.7273 20 6 42 112
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95%
## Overall 0.3493389 0.3647104 0.3833783 0.3960273 0.3958050 0.4074788 0.4267862
## AD 0.3493389 0.3567966 0.3626697 0.3771104 0.3758867 0.3868844 0.3954810
## MCI NA NA NA NA NaN NA NA
## CTL 0.3566807 0.3720598 0.3863418 0.3992158 0.3991678 0.4094520 0.4280006
## Max. SD NAs
## 0.4411069 0.01856467 0
## 0.4034812 0.01439800 0
## NA NA 0
## 0.4411069 0.01704522 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## optimal_cutpoint 0.38 0.38 0.39 0.39 0.39 0.39 0.39 0.40 0.00 0
## AUC_b 0.74 0.79 0.83 0.85 0.85 0.87 0.90 0.95 0.03 0
## AUC_oob 0.69 0.77 0.82 0.85 0.85 0.88 0.92 0.99 0.05 0
## sum_sens_spec_b 1.22 1.38 1.46 1.52 1.52 1.57 1.65 1.83 0.09 0
## sum_sens_spec_oob 1.04 1.24 1.37 1.46 1.46 1.54 1.65 1.92 0.12 0
## acc_b 0.59 0.64 0.70 0.73 0.74 0.78 0.86 0.93 0.06 0
## acc_oob 0.55 0.61 0.68 0.72 0.72 0.77 0.83 0.93 0.07 0
## sensitivity_b 0.53 0.66 0.73 0.78 0.78 0.83 0.91 1.00 0.08 0
## sensitivity_oob 0.17 0.44 0.62 0.75 0.73 0.86 1.00 1.00 0.17 0
## specificity_b 0.56 0.62 0.68 0.73 0.73 0.79 0.87 0.95 0.08 0
## specificity_oob 0.48 0.57 0.66 0.72 0.72 0.79 0.89 0.96 0.10 0
## cohens_kappa_b 0.11 0.20 0.27 0.33 0.34 0.39 0.51 0.72 0.10 0
## cohens_kappa_oob 0.04 0.15 0.23 0.29 0.29 0.35 0.45 0.76 0.09 0
## Method: maximize_boot_metric
## Predictor: logAvgThickness
## Outcome: Diagnostic
## Direction: <=
## Nr. of bootstraps: 1000
##
## AUC n n_pos n_neg
## 0.6401 220 66 154
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## 0.3994 1.1688 0.5455 0.6818 0.487 45 21 79 75
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95%
## Overall 0.3516182 0.3687896 0.3851133 0.3969891 0.3965573 0.4074223 0.4258681
## AD NA NA NA NA NaN NA NA
## MCI 0.3516182 0.3675042 0.3802191 0.3905426 0.3904661 0.4003543 0.4150275
## CTL 0.3566807 0.3720598 0.3863418 0.3992158 0.3991678 0.4094520 0.4280006
## Max. SD NAs
## 0.4411069 0.01693813 0
## NA NA 0
## 0.4220982 0.01513035 0
## 0.4411069 0.01704522 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## optimal_cutpoint 0.39 0.39 0.40 0.40 0.40 0.40 0.40 0.41 0.00 0
## AUC_b 0.51 0.57 0.61 0.64 0.64 0.66 0.70 0.76 0.04 0
## AUC_oob 0.50 0.55 0.61 0.64 0.64 0.68 0.73 0.80 0.05 0
## sum_sens_spec_b 0.95 1.09 1.16 1.20 1.20 1.25 1.32 1.42 0.07 0
## sum_sens_spec_oob 0.86 1.02 1.11 1.17 1.17 1.23 1.33 1.44 0.09 0
## acc_b 0.39 0.48 0.54 0.58 0.57 0.61 0.65 0.71 0.06 0
## acc_oob 0.39 0.46 0.52 0.56 0.56 0.60 0.65 0.73 0.06 0
## sensitivity_b 0.37 0.52 0.61 0.67 0.67 0.75 0.83 0.93 0.10 0
## sensitivity_oob 0.24 0.43 0.56 0.65 0.66 0.76 0.87 1.00 0.13 0
## specificity_b 0.19 0.35 0.46 0.54 0.53 0.61 0.70 0.78 0.10 0
## specificity_oob 0.18 0.32 0.43 0.52 0.52 0.60 0.70 0.81 0.11 0
## cohens_kappa_b -0.04 0.07 0.13 0.17 0.17 0.21 0.27 0.37 0.06 0
## cohens_kappa_oob -0.07 0.02 0.09 0.13 0.14 0.19 0.28 0.38 0.08 0
lab1 = paste("AD: ACC=", signif(cpK$acc,2),"\nSENS=",signif(cpK$sensitivity,2),"\nSPEC=",signif(cpK$specificity,2),"\nMCI: ACC=", signif(cpK_MCI$acc,2),"\nSENS=",signif(cpK_MCI$sensitivity,2),"\nSPEC=",signif(cpK_MCI$specificity,2))
xrng1 <- range(dados_hemi_v1$K)
cutpoint_a <- ggplot(dados_hemi_v1, aes(x = K, color = Diagnostic, fill = Diagnostic, alpha = 0.4))+
geom_density() +
geom_vline(xintercept = cpK$optimal_cutpoint, linetype = "dashed") +
geom_vline(xintercept = cpK_MCI$optimal_cutpoint, linetype = "dotted") +
theme_pubr() +
guides(alpha = "none") +
theme(axis.title = element_text(size = 11),
axis.text = element_text(size = 10), text = element_text(size = 10)) +
scale_x_continuous(
labels = scales::number_format(accuracy = 0.01), limits=c(-0.59,-0.48), breaks = c(-0.58, -0.55, -0.52, -0.49)) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette) +
annotate("text", x = xrng1[1], y = Inf, vjust = 1.1, hjust = 0.95,label = lab1, size = 2)
lab2 = paste("AD: ACC=", signif(cpT$acc,2),"\nSENS=",signif(cpT$sensitivity,2),"\nSPEC=",signif(cpT$specificity,2),"\nMCI: ACC=", signif(cpT_MCI$acc,2),"\nSENS=",signif(cpT_MCI$sensitivity,2),"\nSPEC=",signif(cpT_MCI$specificity,2))
xrng2 <- range(dados_hemi_v1$logAvgThickness)
cutpoint_b <- ggplot(dados_hemi_v1, aes(x = logAvgThickness, color = Diagnostic, fill = Diagnostic, alpha = 0.4))+
geom_density() +
geom_vline(xintercept = cpT$optimal_cutpoint, linetype = "dashed") +
geom_vline(xintercept = cpT_MCI$optimal_cutpoint, linetype = "dotted") +
theme_pubr() +
guides(alpha = "none") +
theme(axis.title = element_text(size = 11),
axis.text = element_text(size = 10), text = element_text(size = 10), legend.position = "none") +
labs(x = expression('log'[10]*'T')) +
labs(x = expression('log'[10]*'T')) + scale_x_continuous(limits=c(0.32,0.48), n.breaks = 4) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette) +
annotate("text", x = xrng2[1], y = Inf, vjust = 1.1, hjust = 0.95, label = lab2, size = 2)
# cutpoint_b
fig_cutpoint <- ggarrange(cutpoint_a, cutpoint_b, labels = c("A", "B"), ncol = 1, font.label = list(size = 11), common.legend = TRUE, legend = "top")
fig_cutpoint
8.0.1.1 Deaged
cpK_deaged <-
cutpointr(
filter(dados_hemi_v1, Diagnostic == "AD" | Diagnostic == "CTL"),
K_age_decay,
Diagnostic,
pos_class = "AD",
neg_class = "CTL",
method = maximize_boot_metric,
metric = sum_sens_spec,
na.rm = TRUE,
boot_runs = 1000,
use_midpoints = TRUE)
# summary(cpK)
# plot(cpK)
cpK_MCI_deaged <-
cutpointr(
filter(dados_hemi_v1, Diagnostic == "MCI" | Diagnostic == "CTL"),
K_age_decay,
Diagnostic,
pos_class = "MCI",
neg_class = "CTL",
method = maximize_boot_metric,
metric = sum_sens_spec,
na.rm = TRUE,
boot_runs = 1000,
use_midpoints = TRUE)
# summary(cpK_MCI)
# plot(cpK_MCI)
cpT_deaged <-
cutpointr(
filter(dados_hemi_v1, Diagnostic == "AD" | Diagnostic == "CTL"),
logAvgThickness_age_decay,
Diagnostic,
pos_class = "AD",
neg_class = "CTL",
method = maximize_boot_metric,
metric = sum_sens_spec,
na.rm = TRUE,
boot_runs = 1000,
use_midpoints = TRUE)
# summary(cpT)
# plot(cpT)
cpT_MCI_deaged <-
cutpointr(
filter(dados_hemi_v1, Diagnostic == "MCI" | Diagnostic == "CTL"),
logAvgThickness_age_decay,
Diagnostic,
pos_class = "MCI",
neg_class = "CTL",
method = maximize_boot_metric,
metric = sum_sens_spec,
na.rm = TRUE,
boot_runs = 1000,
use_midpoints = TRUE)
# summary(cpT_MCI)
# plot(cpT_MCI)lab3 = paste("AD: ACC=", signif(cpK_deaged$acc,2),"\nSENS=",signif(cpK_deaged$sensitivity,2),"\nSPEC=",signif(cpK_deaged$specificity,2),"\nMCI: ACC=", signif(cpK_MCI_deaged$acc,2),"\nSENS=",signif(cpK_MCI_deaged$sensitivity,2),"\nSPEC=",signif(cpK_MCI_deaged$specificity,2))
cutpoint_a_deaged <- ggplot(dados_hemi_v1, aes(x = K_age_decay, color = Diagnostic, fill = Diagnostic, alpha = 0.4))+
geom_density() +
geom_vline(data = cpK_deaged, aes(xintercept = optimal_cutpoint), linetype = "dashed") +
geom_vline(data = cpK_MCI_deaged, aes(xintercept = optimal_cutpoint), linetype = "dotted") +
theme_pubr() +
guides(alpha = "none", linetype = "none") +
theme(axis.title = element_text(size = 11),
axis.text = element_text(size = 10), text = element_text(size = 10)) +
labs(x = "K (After age correction)") + scale_x_continuous(
labels = scales::number_format(accuracy = 0.01), limits=c(-0.59,-0.48), breaks = c(-0.58, -0.55, -0.52, -0.49)) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette) +
annotate("text", x = xrng1[1], y = Inf, vjust = 1.1, hjust = 0.95, label = lab3, size = 2)
# cutpoint_a_deaged
lab4 = paste("AD: ACC=", signif(cpT_deaged$acc,2),"\nSENS=",signif(cpT_deaged$sensitivity,2),"\nSPEC=",signif(cpT_deaged$specificity,2),"\nMCI: ACC=", signif(cpT_MCI_deaged$acc,2),"\nSENS=",signif(cpT_MCI_deaged$sensitivity,2),"\nSPEC=",signif(cpT_MCI_deaged$specificity,2))
cutpoint_b_deaged <- ggplot(dados_hemi_v1, aes(x = logAvgThickness_age_decay, color = Diagnostic, fill = Diagnostic, alpha = 0.4))+
geom_density() +
geom_vline(data = cpT_deaged, aes(xintercept = optimal_cutpoint), linetype = "dashed") +
geom_vline(data = cpT_MCI_deaged, aes(xintercept = optimal_cutpoint), linetype = "dotted") +
theme_pubr() +
guides(alpha = "none", linetype = "none") +
theme(axis.title = element_text(size = 11),
axis.text = element_text(size = 10), text = element_text(size = 10), legend.position = "none") +
labs(x = expression('log'[10]*'T '*('After age correction'))) +
scale_x_continuous(limits=c(0.32,0.48), n.breaks = 4) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette) +
annotate("text", x = xrng2[1], y = Inf, vjust = 1.1, hjust = 0.95, label = lab4, size = 2)
fig_cutpoint_deaged_alt <- ggarrange(cutpoint_a, cutpoint_a_deaged, cutpoint_b, cutpoint_b_deaged, labels = c("A", "B", "C", "D"), ncol = 1, nrow = 4, font.label = list(size = 11), common.legend = TRUE, legend = "top")9 Figure 2
fig_cutpoint_deaged_alt
Optimal cut-off (maximum sensitivity + specificity) for K and Average Cortical Thickness including results with removed age effect (age correction). AD in red (N = 13), MCI in green (N = 33), and Cognitive Unimpaired Controls (CTL) in blue (N = 77). The dashed line represents optimal cut-off to discriminate AD and CTL, and the dotted line represents optimal cut-off to discriminate MCI and CTL. ACC - accuracy, SPEC - specificity, and SENS - sensibility. (A) The optimal cut-off for the CTL-AD contrast is -0.54 and CTL-MCI, -0.53. (B) The optimal cut-off for CTL-AD = -0.52 and CTL-MCI = -0.51. (C) The optimal cut-off for CTL-AD = 0.39 mm and CTL-MCI = 0.40 mm. (D) The optimal cut-off for CTL-AD = 0.43 mm and CTL-MCI = 0.44 mm.
ggsave("fig_cutpoint_deaged_alt.pdf", plot = fig_cutpoint_deaged_alt, dpi=1200, width = 8.7, height = 22, units = "cm", device = "pdf")
ggsave("fig_cutpoint_deaged_alt.pdf", plot = fig_cutpoint_deaged_alt, dpi=1200, width = 8.7, height = 22, units = "cm", device = "pdf")9.0.1 Lobes
9.0.1.1 Cut point - lobes
10 Table S2
dados_lobos_v1$subgroup <- dados_lobos_v1$ROI
cpK <-
cutpointr(
filter(dados_lobos_v1, Diagnostic == "AD" | Diagnostic == "CTL"),
K_corrected,
Diagnostic,
ROI,
pos_class = "AD",
neg_class = "CTL",
method = maximize_boot_metric,
metric = sum_sens_spec,
na.rm = TRUE,
boot_runs = 1000,
use_midpoints = TRUE
)
summary(cpK)## Method: maximize_boot_metric
## Predictor: K_corrected
## Outcome: Diagnostic
## Direction: <=
## Subgroups: F, P, T, O
## Nr. of bootstraps: 1000
##
## Subgroup: F
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.7511 177 26 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## -0.5505 1.3227 0.6667 0.6538 0.6689 17 9 50 101
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu.
## Overall -0.5948952 -0.5741638 -0.5569396 -0.5445030 -0.5448128 -0.5314285
## AD -0.5948952 -0.5918458 -0.5743711 -0.5572738 -0.5604638 -0.5461758
## MCI NA NA NA NA NaN NA
## CTL -0.5777982 -0.5689791 -0.5558189 -0.5411732 -0.5421180 -0.5284866
## 95% Max. SD NAs
## -0.5183209 -0.5040702 0.01828324 0
## -0.5310599 -0.5236191 0.01992406 0
## NA NA NA 0
## -0.5176837 -0.5040702 0.01662191 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## optimal_cutpoint -0.57 -0.56 -0.56 -0.55 -0.55 -0.55 -0.54 -0.54 0.01
## AUC_b 0.52 0.66 0.71 0.75 0.75 0.79 0.83 0.90 0.05
## AUC_oob 0.44 0.63 0.71 0.75 0.75 0.80 0.87 0.99 0.07
## sum_sens_spec_b 0.97 1.17 1.28 1.35 1.35 1.43 1.53 1.63 0.11
## sum_sens_spec_oob 0.75 1.05 1.20 1.30 1.30 1.39 1.53 1.75 0.14
## acc_b 0.40 0.58 0.65 0.71 0.71 0.76 0.84 0.95 0.08
## acc_oob 0.42 0.54 0.64 0.70 0.69 0.76 0.83 0.89 0.09
## sensitivity_b 0.32 0.43 0.55 0.64 0.63 0.71 0.81 0.97 0.12
## sensitivity_oob 0.00 0.25 0.44 0.60 0.59 0.73 0.89 1.00 0.20
## specificity_b 0.36 0.55 0.65 0.72 0.72 0.80 0.89 0.98 0.11
## specificity_oob 0.33 0.50 0.63 0.72 0.71 0.79 0.90 0.98 0.12
## cohens_kappa_b -0.01 0.10 0.18 0.24 0.25 0.30 0.43 0.63 0.10
## cohens_kappa_oob -0.16 0.03 0.13 0.19 0.19 0.26 0.37 0.52 0.10
## NAs
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
##
## Subgroup: P
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.824 177 26 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## -0.5222 1.4671 0.7627 0.6923 0.7748 18 8 34 117
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu.
## Overall -0.5753676 -0.5497660 -0.5277313 -0.5107007 -0.5114334 -0.4938295
## AD -0.5753676 -0.5711317 -0.5484500 -0.5313049 -0.5344850 -0.5217755
## MCI NA NA NA NA NaN NA
## CTL -0.5551769 -0.5406598 -0.5193854 -0.5069022 -0.5074642 -0.4923347
## 95% Max. SD NAs
## -0.4770092 -0.4638806 0.02220156 0
## -0.5046080 -0.4986455 0.02042450 0
## NA NA NA 0
## -0.4768356 -0.4638806 0.02002138 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## optimal_cutpoint -0.54 -0.53 -0.52 -0.52 -0.52 -0.52 -0.51 -0.50 0.00
## AUC_b 0.63 0.75 0.80 0.83 0.83 0.85 0.89 0.92 0.04
## AUC_oob 0.61 0.72 0.79 0.82 0.82 0.86 0.91 0.97 0.06
## sum_sens_spec_b 1.13 1.33 1.45 1.52 1.51 1.57 1.66 1.77 0.10
## sum_sens_spec_oob 0.97 1.22 1.37 1.46 1.45 1.55 1.66 1.85 0.13
## acc_b 0.46 0.63 0.73 0.77 0.75 0.80 0.83 0.88 0.06
## acc_oob 0.47 0.62 0.71 0.75 0.74 0.78 0.83 0.91 0.06
## sensitivity_b 0.52 0.62 0.71 0.76 0.75 0.80 0.87 0.97 0.07
## sensitivity_oob 0.17 0.44 0.60 0.71 0.71 0.82 1.00 1.00 0.16
## specificity_b 0.40 0.61 0.72 0.77 0.75 0.80 0.84 0.91 0.07
## specificity_oob 0.39 0.58 0.71 0.76 0.75 0.80 0.86 0.98 0.09
## cohens_kappa_b 0.06 0.18 0.29 0.35 0.35 0.41 0.48 0.60 0.09
## cohens_kappa_oob -0.01 0.15 0.24 0.30 0.31 0.37 0.47 0.70 0.10
## NAs
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
##
## Subgroup: T
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.8097 177 26 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## -0.5108 1.4249 0.6723 0.7692 0.6556 20 6 52 99
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu.
## Overall -0.5947243 -0.5401430 -0.5198351 -0.5067115 -0.5066949 -0.4911627
## AD -0.5947243 -0.5671082 -0.5379738 -0.5261631 -0.5287671 -0.5129811
## MCI NA NA NA NA NaN NA
## CTL -0.5579907 -0.5320530 -0.5173996 -0.5022748 -0.5028944 -0.4888990
## 95% Max. SD NAs
## -0.4728609 -0.4601226 0.02184019 0
## -0.4992148 -0.4960854 0.02298717 0
## NA NA NA 0
## -0.4718815 -0.4601226 0.01930335 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## optimal_cutpoint -0.53 -0.52 -0.52 -0.51 -0.51 -0.51 -0.51 -0.50 0.01
## AUC_b 0.66 0.74 0.79 0.81 0.81 0.84 0.87 0.92 0.04
## AUC_oob 0.44 0.71 0.78 0.81 0.81 0.85 0.90 0.98 0.06
## sum_sens_spec_b 1.14 1.31 1.39 1.45 1.45 1.51 1.59 1.73 0.09
## sum_sens_spec_oob 0.70 1.15 1.29 1.39 1.38 1.47 1.60 1.81 0.14
## acc_b 0.48 0.60 0.66 0.70 0.70 0.74 0.82 0.92 0.07
## acc_oob 0.42 0.57 0.64 0.69 0.69 0.74 0.81 0.90 0.07
## sensitivity_b 0.44 0.58 0.68 0.76 0.75 0.83 0.90 1.00 0.10
## sensitivity_oob 0.00 0.36 0.56 0.70 0.69 0.82 1.00 1.00 0.19
## specificity_b 0.40 0.56 0.64 0.69 0.70 0.75 0.85 0.95 0.08
## specificity_oob 0.32 0.53 0.62 0.68 0.69 0.76 0.87 1.00 0.10
## cohens_kappa_b 0.08 0.16 0.22 0.27 0.28 0.33 0.43 0.64 0.08
## cohens_kappa_oob -0.11 0.09 0.18 0.23 0.23 0.29 0.39 0.51 0.09
## NAs
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
##
## Subgroup: O
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.6882 177 26 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## -0.4975 1.4047 0.791 0.5769 0.8278 15 11 26 125
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu.
## Overall -0.5532809 -0.5253072 -0.4962470 -0.4817953 -0.4829495 -0.4698824
## AD -0.5532809 -0.5338382 -0.5230440 -0.5001447 -0.4975855 -0.4713651
## MCI NA NA NA NA NaN NA
## CTL -0.5299648 -0.5143920 -0.4932941 -0.4800622 -0.4804294 -0.4699315
## 95% Max. SD NAs
## -0.4463241 -0.4209974 0.02293166 0
## -0.4525778 -0.4390378 0.02999238 0
## NA NA NA 0
## -0.4463146 -0.4209974 0.02058064 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## optimal_cutpoint -0.52 -0.51 -0.50 -0.50 -0.50 -0.50 -0.49 -0.49 0.00
## AUC_b 0.45 0.57 0.64 0.69 0.69 0.74 0.80 0.88 0.07
## AUC_oob 0.40 0.52 0.62 0.69 0.69 0.76 0.85 0.98 0.10
## sum_sens_spec_b 1.06 1.20 1.31 1.39 1.39 1.46 1.55 1.71 0.11
## sum_sens_spec_oob 0.84 1.09 1.23 1.32 1.33 1.42 1.56 1.82 0.14
## acc_b 0.60 0.70 0.76 0.80 0.79 0.83 0.86 0.92 0.05
## acc_oob 0.54 0.67 0.74 0.78 0.78 0.82 0.86 0.92 0.06
## sensitivity_b 0.20 0.37 0.48 0.55 0.55 0.64 0.74 0.88 0.11
## sensitivity_oob 0.00 0.25 0.40 0.50 0.50 0.62 0.75 1.00 0.16
## specificity_b 0.61 0.72 0.80 0.84 0.83 0.88 0.92 0.97 0.06
## specificity_oob 0.54 0.69 0.78 0.83 0.82 0.88 0.94 0.98 0.08
## cohens_kappa_b 0.04 0.16 0.25 0.32 0.32 0.39 0.47 0.64 0.10
## cohens_kappa_oob -0.13 0.07 0.18 0.27 0.27 0.35 0.46 0.66 0.12
## NAs
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0plot(cpK)
cpK_MCI <-
cutpointr(
filter(dados_lobos_v1, Diagnostic == "MCI" | Diagnostic == "CTL"),
K_corrected,
Diagnostic,
ROI,
pos_class = "MCI",
neg_class = "CTL",
method = maximize_boot_metric,
metric = sum_sens_spec,
na.rm = TRUE,
boot_runs = 1000,
use_midpoints = TRUE
)
summary(cpK_MCI)## Method: maximize_boot_metric
## Predictor: K_corrected
## Outcome: Diagnostic
## Direction: <=
## Subgroups: F, P, T, O
## Nr. of bootstraps: 1000
##
## Subgroup: F
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.587 216 65 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## -0.5412 1.1648 0.5509 0.6615 0.5033 43 22 75 76
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu.
## Overall -0.5781076 -0.5708354 -0.5559783 -0.5441751 -0.5436111 -0.5312231
## AD NA NA NA NA NaN NA
## MCI -0.5781076 -0.5746358 -0.5580436 -0.5484470 -0.5470797 -0.5369166
## CTL -0.5777982 -0.5689791 -0.5558189 -0.5411732 -0.5421180 -0.5284866
## 95% Max. SD NAs
## -0.5179243 -0.5040702 0.01673165 0
## NA NA NA 0
## -0.5201961 -0.5115329 0.01659585 0
## -0.5176837 -0.5040702 0.01662191 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## optimal_cutpoint -0.56 -0.55 -0.55 -0.54 -0.54 -0.54 -0.53 -0.53 0.00
## AUC_b 0.45 0.52 0.56 0.59 0.59 0.61 0.66 0.74 0.04
## AUC_oob 0.37 0.50 0.55 0.59 0.59 0.63 0.68 0.76 0.06
## sum_sens_spec_b 0.89 1.03 1.10 1.16 1.16 1.21 1.29 1.41 0.08
## sum_sens_spec_oob 0.74 0.96 1.06 1.12 1.12 1.18 1.28 1.38 0.10
## acc_b 0.40 0.46 0.52 0.56 0.55 0.59 0.63 0.70 0.05
## acc_oob 0.32 0.44 0.50 0.54 0.54 0.58 0.63 0.71 0.06
## sensitivity_b 0.21 0.46 0.58 0.65 0.64 0.71 0.79 0.89 0.10
## sensitivity_oob 0.15 0.39 0.53 0.62 0.61 0.71 0.83 1.00 0.13
## specificity_b 0.26 0.34 0.46 0.52 0.52 0.58 0.68 0.83 0.10
## specificity_oob 0.16 0.30 0.43 0.51 0.50 0.58 0.69 0.85 0.11
## cohens_kappa_b -0.12 0.03 0.08 0.13 0.13 0.17 0.24 0.35 0.07
## cohens_kappa_oob -0.25 -0.04 0.04 0.10 0.10 0.15 0.23 0.34 0.08
## NAs
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
##
## Subgroup: P
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.4995 216 65 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## -0.4988 1.0149 0.4583 0.6308 0.3841 41 24 93 58
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu.
## Overall -0.5755226 -0.5408819 -0.5192216 -0.5062234 -0.5077524 -0.4923294
## AD NA NA NA NA NaN NA
## MCI -0.5755226 -0.5460118 -0.5189791 -0.5047562 -0.5084220 -0.4923564
## CTL -0.5551769 -0.5406598 -0.5193854 -0.5069022 -0.5074642 -0.4923347
## 95% Max. SD NAs
## -0.4770073 -0.4638806 0.02041888 0
## NA NA NA 0
## -0.4825298 -0.4661993 0.02145856 0
## -0.4768356 -0.4638806 0.02002138 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## optimal_cutpoint -0.55 -0.53 -0.51 -0.50 -0.51 -0.50 -0.49 -0.48 0.01
## AUC_b 0.34 0.43 0.47 0.50 0.50 0.53 0.58 0.62 0.04
## AUC_oob 0.30 0.40 0.46 0.50 0.50 0.54 0.60 0.71 0.06
## sum_sens_spec_b 0.77 0.89 0.97 1.02 1.02 1.08 1.15 1.25 0.08
## sum_sens_spec_oob 0.65 0.81 0.91 0.98 0.97 1.04 1.13 1.39 0.10
## acc_b 0.26 0.38 0.44 0.49 0.50 0.55 0.63 0.72 0.08
## acc_oob 0.19 0.35 0.41 0.47 0.48 0.54 0.64 0.78 0.09
## sensitivity_b 0.04 0.18 0.38 0.57 0.54 0.71 0.84 0.99 0.21
## sensitivity_oob 0.00 0.12 0.33 0.52 0.51 0.68 0.87 1.00 0.23
## specificity_b 0.12 0.18 0.34 0.45 0.48 0.60 0.82 0.99 0.19
## specificity_oob 0.04 0.15 0.31 0.44 0.46 0.60 0.83 1.00 0.21
## cohens_kappa_b -0.19 -0.10 -0.03 0.01 0.01 0.06 0.12 0.20 0.06
## cohens_kappa_oob -0.30 -0.17 -0.08 -0.02 -0.02 0.03 0.10 0.25 0.08
## NAs
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
##
## Subgroup: T
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.6738 216 65 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## -0.5074 1.2223 0.5972 0.6462 0.5762 42 23 64 87
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu.
## Overall -0.5579907 -0.5357395 -0.5218213 -0.5066437 -0.5063702 -0.4918393
## AD NA NA NA NA NaN NA
## MCI -0.5492814 -0.5407649 -0.5278604 -0.5155029 -0.5144448 -0.4986739
## CTL -0.5579907 -0.5320530 -0.5173996 -0.5022748 -0.5028944 -0.4888990
## 95% Max. SD NAs
## -0.4746984 -0.4601226 0.01961174 0
## NA NA NA 0
## -0.4811155 -0.4745659 0.01800097 0
## -0.4718815 -0.4601226 0.01930335 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## optimal_cutpoint -0.52 -0.52 -0.51 -0.51 -0.51 -0.50 -0.50 -0.49 0.01
## AUC_b 0.56 0.60 0.65 0.67 0.67 0.70 0.74 0.79 0.04
## AUC_oob 0.45 0.58 0.64 0.67 0.67 0.71 0.76 0.82 0.05
## sum_sens_spec_b 1.01 1.13 1.20 1.25 1.25 1.30 1.37 1.53 0.07
## sum_sens_spec_oob 0.90 1.05 1.14 1.21 1.21 1.27 1.36 1.46 0.09
## acc_b 0.46 0.52 0.58 0.62 0.62 0.66 0.71 0.78 0.06
## acc_oob 0.39 0.50 0.56 0.60 0.60 0.64 0.69 0.77 0.06
## sensitivity_b 0.35 0.50 0.58 0.65 0.65 0.71 0.79 0.94 0.09
## sensitivity_oob 0.11 0.39 0.50 0.61 0.61 0.71 0.83 1.00 0.14
## specificity_b 0.30 0.42 0.54 0.61 0.61 0.69 0.77 0.86 0.11
## specificity_oob 0.22 0.38 0.51 0.61 0.60 0.68 0.78 0.93 0.12
## cohens_kappa_b 0.00 0.11 0.17 0.22 0.22 0.27 0.35 0.47 0.07
## cohens_kappa_oob -0.10 0.05 0.12 0.18 0.18 0.23 0.32 0.42 0.08
## NAs
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
##
## Subgroup: O
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.5561 216 65 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## -0.48 1.1428 0.5417 0.6462 0.4967 42 23 76 75
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu.
## Overall -0.5299648 -0.5151736 -0.4939268 -0.4814026 -0.4814872 -0.4706468
## AD NA NA NA NA NaN NA
## MCI -0.5197871 -0.5148114 -0.4950804 -0.4847680 -0.4839446 -0.4742901
## CTL -0.5299648 -0.5143920 -0.4932941 -0.4800622 -0.4804294 -0.4699315
## 95% Max. SD NAs
## -0.4463549 -0.4209974 0.01977248 0
## NA NA NA 0
## -0.4507341 -0.4435537 0.01765937 0
## -0.4463146 -0.4209974 0.02058064 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## optimal_cutpoint -0.50 -0.49 -0.48 -0.48 -0.48 -0.48 -0.47 -0.46 0.00
## AUC_b 0.43 0.49 0.53 0.56 0.56 0.59 0.62 0.73 0.04
## AUC_oob 0.36 0.46 0.52 0.55 0.55 0.59 0.64 0.71 0.06
## sum_sens_spec_b 0.85 1.00 1.08 1.13 1.13 1.18 1.26 1.44 0.08
## sum_sens_spec_oob 0.76 0.92 1.01 1.08 1.08 1.14 1.24 1.36 0.10
## acc_b 0.33 0.43 0.50 0.54 0.53 0.57 0.62 0.69 0.06
## acc_oob 0.34 0.42 0.47 0.51 0.51 0.55 0.59 0.67 0.05
## sensitivity_b 0.27 0.42 0.59 0.67 0.65 0.73 0.82 0.93 0.11
## sensitivity_oob 0.09 0.35 0.52 0.63 0.62 0.72 0.85 1.00 0.15
## specificity_b 0.13 0.28 0.40 0.49 0.48 0.55 0.64 0.82 0.11
## specificity_oob 0.11 0.27 0.38 0.46 0.46 0.54 0.65 0.86 0.12
## cohens_kappa_b -0.14 0.00 0.06 0.10 0.10 0.15 0.21 0.38 0.06
## cohens_kappa_oob -0.23 -0.07 0.01 0.06 0.06 0.11 0.19 0.31 0.08
## NAs
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0plot(cpK_MCI)
cpT <-
cutpointr(
filter(dados_lobos_v1, Diagnostic == "AD" | Diagnostic == "CTL"),
logAvgThickness,
Diagnostic,
ROI,
pos_class = "AD",
neg_class = "CTL",
method = maximize_boot_metric,
metric = sum_sens_spec,
na.rm = TRUE,
boot_runs = 1000,
use_midpoints = TRUE
)
summary(cpT)## Method: maximize_boot_metric
## Predictor: logAvgThickness
## Outcome: Diagnostic
## Direction: <=
## Subgroups: F, P, T, O
## Nr. of bootstraps: 1000
##
## Subgroup: F
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.8036 177 26 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## 0.3901 1.54 0.8249 0.6923 0.8477 18 8 23 128
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95%
## Overall 0.3573518 0.3705455 0.3912588 0.4023742 0.4034546 0.4148566 0.4402068
## AD 0.3629861 0.3661257 0.3716468 0.3824120 0.3849235 0.3961625 0.4138817
## MCI NA NA NA NA NaN NA NA
## CTL 0.3573518 0.3751557 0.3953965 0.4047286 0.4066454 0.4164345 0.4430437
## Max. SD NAs
## 0.4536341 0.02019155 0
## 0.4185876 0.01642300 0
## NA NA 0
## 0.4536341 0.01906939 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## optimal_cutpoint 0.38 0.38 0.39 0.39 0.39 0.39 0.40 0.41 0.00 0
## AUC_b 0.63 0.72 0.77 0.81 0.80 0.84 0.88 0.94 0.05 0
## AUC_oob 0.58 0.69 0.76 0.81 0.80 0.85 0.91 1.00 0.07 0
## sum_sens_spec_b 1.12 1.35 1.45 1.51 1.51 1.58 1.68 1.83 0.10 0
## sum_sens_spec_oob 0.97 1.20 1.36 1.46 1.45 1.55 1.69 1.86 0.15 0
## acc_b 0.53 0.71 0.77 0.81 0.80 0.84 0.87 0.93 0.05 0
## acc_oob 0.57 0.69 0.76 0.79 0.79 0.83 0.87 0.91 0.05 0
## sensitivity_b 0.38 0.54 0.63 0.70 0.69 0.75 0.83 0.96 0.09 0
## sensitivity_oob 0.12 0.33 0.50 0.67 0.64 0.75 0.89 1.00 0.17 0
## specificity_b 0.48 0.72 0.79 0.83 0.82 0.86 0.90 0.96 0.06 0
## specificity_oob 0.51 0.68 0.78 0.83 0.82 0.87 0.91 0.98 0.07 0
## cohens_kappa_b 0.06 0.24 0.33 0.40 0.40 0.47 0.56 0.69 0.10 0
## cohens_kappa_oob -0.02 0.16 0.28 0.35 0.35 0.43 0.53 0.67 0.11 0
##
## Subgroup: P
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.786 177 26 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## 0.3602 1.527 0.678 0.8846 0.6424 23 3 54 97
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95%
## Overall 0.3208179 0.3363368 0.3520451 0.3643827 0.3645260 0.3762276 0.3982727
## AD 0.3259872 0.3290103 0.3412247 0.3510509 0.3491394 0.3558869 0.3660903
## MCI NA NA NA NA NaN NA NA
## CTL 0.3208179 0.3365847 0.3535749 0.3676422 0.3671754 0.3784593 0.3994138
## Max. SD NAs
## 0.4154907 0.01896322 0
## 0.3698453 0.01116366 0
## NA NA 0
## 0.4154907 0.01878994 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## optimal_cutpoint 0.35 0.36 0.36 0.36 0.36 0.36 0.36 0.37 0.00 0
## AUC_b 0.64 0.72 0.76 0.79 0.79 0.81 0.84 0.87 0.04 0
## AUC_oob 0.59 0.70 0.75 0.79 0.79 0.82 0.87 0.98 0.05 0
## sum_sens_spec_b 1.20 1.39 1.48 1.53 1.53 1.58 1.65 1.73 0.08 0
## sum_sens_spec_oob 0.97 1.25 1.42 1.51 1.49 1.58 1.67 1.76 0.13 0
## acc_b 0.51 0.60 0.65 0.68 0.68 0.71 0.74 0.80 0.04 0
## acc_oob 0.49 0.58 0.64 0.67 0.67 0.70 0.75 0.81 0.05 0
## sensitivity_b 0.67 0.81 0.86 0.89 0.89 0.93 0.97 1.00 0.05 0
## sensitivity_oob 0.20 0.60 0.79 0.88 0.85 1.00 1.00 1.00 0.14 0
## specificity_b 0.46 0.55 0.61 0.64 0.64 0.67 0.72 0.78 0.05 0
## specificity_oob 0.42 0.53 0.60 0.64 0.64 0.68 0.74 0.86 0.06 0
## cohens_kappa_b 0.09 0.18 0.25 0.29 0.29 0.33 0.40 0.52 0.07 0
## cohens_kappa_oob -0.02 0.13 0.21 0.27 0.27 0.32 0.40 0.55 0.08 0
##
## Subgroup: T
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.89 177 26 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## 0.4416 1.5362 0.7401 0.8077 0.7285 21 5 41 110
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95%
## Overall 0.3638006 0.4077181 0.4355525 0.4494434 0.4485649 0.4634193 0.4831833
## AD 0.3638006 0.3863285 0.4003040 0.4174322 0.4180355 0.4369206 0.4461635
## MCI NA NA NA NA NaN NA NA
## CTL 0.4050407 0.4239007 0.4409264 0.4543818 0.4538216 0.4649956 0.4844813
## Max. SD NAs
## 0.4986993 0.02311740 0
## 0.4514959 0.02305291 0
## NA NA 0
## 0.4986993 0.01868425 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## optimal_cutpoint 0.42 0.43 0.44 0.44 0.44 0.44 0.45 0.45 0.00 0
## AUC_b 0.78 0.84 0.87 0.89 0.89 0.91 0.94 0.97 0.03 0
## AUC_oob 0.75 0.82 0.86 0.89 0.89 0.92 0.95 1.00 0.04 0
## sum_sens_spec_b 1.35 1.44 1.53 1.59 1.59 1.64 1.72 1.80 0.08 0
## sum_sens_spec_oob 1.05 1.28 1.45 1.54 1.53 1.63 1.74 1.90 0.14 0
## acc_b 0.60 0.69 0.74 0.77 0.78 0.81 0.88 0.94 0.06 0
## acc_oob 0.54 0.66 0.72 0.76 0.76 0.80 0.86 0.92 0.06 0
## sensitivity_b 0.54 0.68 0.76 0.82 0.82 0.88 0.94 1.00 0.08 0
## sensitivity_oob 0.18 0.43 0.67 0.80 0.77 0.90 1.00 1.00 0.18 0
## specificity_b 0.55 0.67 0.72 0.77 0.77 0.82 0.89 0.97 0.07 0
## specificity_oob 0.47 0.62 0.70 0.75 0.76 0.82 0.91 1.00 0.09 0
## cohens_kappa_b 0.16 0.26 0.34 0.39 0.40 0.46 0.57 0.78 0.09 0
## cohens_kappa_oob 0.03 0.20 0.29 0.35 0.36 0.42 0.53 0.75 0.10 0
##
## Subgroup: O
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.6821 177 26 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## 0.3046 1.3016 0.6215 0.6923 0.6093 18 8 59 92
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95%
## Overall 0.2562338 0.2713524 0.2930485 0.3071984 0.3079301 0.3235285 0.3414299
## AD 0.2608827 0.2672465 0.2817425 0.2975927 0.2965027 0.3080038 0.3248181
## MCI NA NA NA NA NaN NA NA
## CTL 0.2562338 0.2731140 0.2954743 0.3097651 0.3098977 0.3256400 0.3445245
## Max. SD NAs
## 0.373209 0.02179725 0
## 0.334749 0.01846444 0
## NA NA 0
## 0.373209 0.02177428 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## optimal_cutpoint 0.28 0.30 0.30 0.30 0.30 0.31 0.31 0.32 0.00 0
## AUC_b 0.50 0.60 0.65 0.68 0.68 0.72 0.77 0.89 0.05 0
## AUC_oob 0.41 0.56 0.63 0.68 0.68 0.73 0.80 0.95 0.08 0
## sum_sens_spec_b 0.94 1.13 1.23 1.30 1.30 1.37 1.47 1.64 0.10 0
## sum_sens_spec_oob 0.73 0.97 1.13 1.23 1.23 1.33 1.47 1.71 0.15 0
## acc_b 0.38 0.50 0.58 0.63 0.63 0.68 0.73 0.83 0.07 0
## acc_oob 0.34 0.47 0.56 0.61 0.61 0.66 0.72 0.82 0.08 0
## sensitivity_b 0.29 0.50 0.62 0.70 0.69 0.76 0.85 0.96 0.11 0
## sensitivity_oob 0.00 0.29 0.50 0.63 0.62 0.78 0.92 1.00 0.19 0
## specificity_b 0.32 0.46 0.55 0.62 0.61 0.69 0.75 0.91 0.09 0
## specificity_oob 0.25 0.43 0.53 0.61 0.60 0.68 0.77 0.95 0.11 0
## cohens_kappa_b -0.03 0.06 0.12 0.17 0.17 0.21 0.29 0.41 0.07 0
## cohens_kappa_oob -0.14 -0.02 0.07 0.13 0.12 0.18 0.26 0.43 0.08 0plot(cpT)
cpT_MCI <-
cutpointr(
filter(dados_lobos_v1, Diagnostic == "MCI" | Diagnostic == "CTL"),
logAvgThickness,
Diagnostic,
ROI,
pos_class = "MCI",
neg_class = "CTL",
method = maximize_boot_metric,
metric = sum_sens_spec,
na.rm = TRUE,
boot_runs = 1000,
use_midpoints = TRUE
)
summary(cpT_MCI)## Method: maximize_boot_metric
## Predictor: logAvgThickness
## Outcome: Diagnostic
## Direction: <=
## Subgroups: F, P, T, O
## Nr. of bootstraps: 1000
##
## Subgroup: F
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.6053 216 65 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## 0.4037 1.099 0.5417 0.5692 0.5298 37 28 71 80
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95%
## Overall 0.3573518 0.3737044 0.3922699 0.4034387 0.4043320 0.4148788 0.4393955
## AD NA NA NA NA NaN NA NA
## MCI 0.3591979 0.3664164 0.3892379 0.4007419 0.3989577 0.4097565 0.4264739
## CTL 0.3573518 0.3751557 0.3953965 0.4047286 0.4066454 0.4164345 0.4430437
## Max. SD NAs
## 0.4536341 0.01891520 0
## NA NA 0
## 0.4314183 0.01754102 0
## 0.4536341 0.01906939 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## optimal_cutpoint 0.39 0.39 0.40 0.40 0.40 0.41 0.41 0.43 0.01 0
## AUC_b 0.47 0.53 0.58 0.61 0.61 0.64 0.67 0.74 0.04 0
## AUC_oob 0.44 0.51 0.57 0.60 0.60 0.64 0.69 0.76 0.05 0
## sum_sens_spec_b 0.91 1.02 1.10 1.16 1.15 1.21 1.27 1.38 0.07 0
## sum_sens_spec_oob 0.79 0.94 1.04 1.10 1.10 1.16 1.25 1.38 0.09 0
## acc_b 0.31 0.45 0.52 0.56 0.56 0.60 0.67 0.76 0.07 0
## acc_oob 0.33 0.43 0.49 0.53 0.54 0.58 0.64 0.73 0.07 0
## sensitivity_b 0.27 0.41 0.52 0.62 0.62 0.72 0.82 0.97 0.13 0
## sensitivity_oob 0.12 0.32 0.46 0.59 0.58 0.70 0.88 1.00 0.17 0
## specificity_b 0.14 0.32 0.45 0.52 0.54 0.64 0.76 0.88 0.14 0
## specificity_oob 0.07 0.29 0.42 0.50 0.51 0.62 0.75 0.93 0.15 0
## cohens_kappa_b -0.06 0.02 0.09 0.13 0.13 0.17 0.24 0.33 0.07 0
## cohens_kappa_oob -0.16 -0.05 0.03 0.08 0.08 0.13 0.21 0.37 0.08 0
##
## Subgroup: P
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.5726 216 65 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## 0.3692 1.1426 0.5231 0.6923 0.4503 45 20 83 68
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95%
## Overall 0.3063354 0.3365749 0.3527768 0.3661864 0.3654310 0.3763397 0.3973651
## AD NA NA NA NA NaN NA NA
## MCI 0.3063354 0.3322565 0.3525954 0.3640680 0.3613786 0.3725529 0.3892357
## CTL 0.3208179 0.3365847 0.3535749 0.3676422 0.3671754 0.3784593 0.3994138
## Max. SD NAs
## 0.4154907 0.01883489 0
## NA NA 0
## 0.3971472 0.01845001 0
## 0.4154907 0.01878994 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## optimal_cutpoint 0.34 0.36 0.37 0.37 0.37 0.37 0.38 0.38 0.00 0
## AUC_b 0.46 0.50 0.54 0.57 0.57 0.60 0.64 0.70 0.04 0
## AUC_oob 0.40 0.49 0.54 0.57 0.57 0.61 0.66 0.73 0.05 0
## sum_sens_spec_b 0.90 1.03 1.10 1.15 1.15 1.19 1.26 1.38 0.07 0
## sum_sens_spec_oob 0.77 0.94 1.04 1.11 1.10 1.17 1.25 1.40 0.09 0
## acc_b 0.33 0.44 0.49 0.52 0.52 0.55 0.60 0.70 0.05 0
## acc_oob 0.34 0.42 0.47 0.50 0.50 0.54 0.59 0.71 0.05 0
## sensitivity_b 0.13 0.47 0.65 0.73 0.71 0.80 0.86 0.94 0.12 0
## sensitivity_oob 0.09 0.39 0.59 0.70 0.67 0.78 0.88 1.00 0.15 0
## specificity_b 0.14 0.27 0.37 0.43 0.44 0.50 0.62 0.89 0.10 0
## specificity_oob 0.13 0.26 0.35 0.42 0.43 0.50 0.63 0.94 0.12 0
## cohens_kappa_b -0.09 0.02 0.08 0.11 0.11 0.15 0.21 0.32 0.06 0
## cohens_kappa_oob -0.20 -0.05 0.04 0.08 0.08 0.13 0.19 0.32 0.07 0
##
## Subgroup: T
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.6889 216 65 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## 0.4404 1.3308 0.7037 0.5692 0.7616 37 28 36 115
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95%
## Overall 0.3828083 0.4162291 0.4372204 0.4501150 0.4496988 0.4628756 0.4820372
## AD NA NA NA NA NaN NA NA
## MCI 0.3828083 0.4059276 0.4282558 0.4385578 0.4401212 0.4552104 0.4709293
## CTL 0.4050407 0.4239007 0.4409264 0.4543818 0.4538216 0.4649956 0.4844813
## Max. SD NAs
## 0.4986993 0.02008186 0
## NA NA 0
## 0.4824209 0.02008228 0
## 0.4986993 0.01868425 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## optimal_cutpoint 0.43 0.44 0.44 0.44 0.44 0.44 0.45 0.46 0.00 0
## AUC_b 0.51 0.62 0.66 0.69 0.69 0.72 0.75 0.81 0.04 0
## AUC_oob 0.51 0.61 0.66 0.69 0.69 0.72 0.77 0.85 0.05 0
## sum_sens_spec_b 0.94 1.18 1.28 1.34 1.33 1.39 1.46 1.58 0.09 0
## sum_sens_spec_oob 1.01 1.14 1.24 1.31 1.30 1.37 1.45 1.59 0.09 0
## acc_b 0.47 0.60 0.67 0.70 0.70 0.73 0.76 0.81 0.05 0
## acc_oob 0.47 0.58 0.66 0.69 0.68 0.72 0.76 0.81 0.05 0
## sensitivity_b 0.36 0.49 0.56 0.59 0.59 0.63 0.69 0.78 0.06 0
## sensitivity_oob 0.22 0.41 0.52 0.58 0.57 0.64 0.74 0.87 0.10 0
## specificity_b 0.47 0.61 0.71 0.75 0.74 0.79 0.83 0.88 0.07 0
## specificity_oob 0.34 0.56 0.69 0.74 0.73 0.79 0.85 0.90 0.08 0
## cohens_kappa_b -0.06 0.16 0.26 0.32 0.32 0.38 0.45 0.54 0.09 0
## cohens_kappa_oob 0.01 0.13 0.23 0.29 0.29 0.35 0.43 0.55 0.09 0
##
## Subgroup: O
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.5585 216 65 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## 0.3176 1.1157 0.4676 0.7846 0.3311 51 14 101 50
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu. 95%
## Overall 0.2562338 0.2722319 0.2952212 0.3075814 0.3085263 0.3222856 0.3406063
## AD NA NA NA NA NaN NA NA
## MCI 0.2648261 0.2725975 0.2949457 0.3050174 0.3053404 0.3170994 0.3336167
## CTL 0.2562338 0.2731140 0.2954743 0.3097651 0.3098977 0.3256400 0.3445245
## Max. SD NAs
## 0.3732090 0.02072181 0
## NA NA 0
## 0.3429703 0.01779132 0
## 0.3732090 0.02177428 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD NAs
## optimal_cutpoint 0.29 0.30 0.31 0.32 0.31 0.32 0.32 0.33 0.01 0
## AUC_b 0.44 0.49 0.53 0.56 0.56 0.59 0.63 0.72 0.04 0
## AUC_oob 0.33 0.47 0.52 0.56 0.56 0.60 0.64 0.75 0.05 0
## sum_sens_spec_b 0.86 1.00 1.07 1.12 1.12 1.17 1.23 1.41 0.07 0
## sum_sens_spec_oob 0.71 0.91 1.01 1.08 1.07 1.14 1.22 1.32 0.10 0
## acc_b 0.35 0.42 0.46 0.50 0.50 0.53 0.60 0.72 0.06 0
## acc_oob 0.28 0.40 0.44 0.48 0.48 0.52 0.57 0.67 0.05 0
## sensitivity_b 0.23 0.51 0.62 0.70 0.71 0.80 0.89 0.96 0.12 0
## sensitivity_oob 0.16 0.41 0.57 0.68 0.67 0.79 0.90 1.00 0.16 0
## specificity_b 0.11 0.26 0.32 0.39 0.41 0.50 0.63 0.84 0.12 0
## specificity_oob 0.12 0.23 0.31 0.39 0.40 0.48 0.60 0.79 0.11 0
## cohens_kappa_b -0.11 0.00 0.05 0.09 0.09 0.13 0.18 0.39 0.06 0
## cohens_kappa_oob -0.20 -0.07 0.01 0.06 0.05 0.10 0.17 0.28 0.07 0plot(cpT_MCI)
dat_text <- data.frame(label = c("4 cylinders", "6 cylinders", "8 cylinders"),
cyl = c(4, 6, 8))
cutpoint_a_lobes <-
ggplot(dados_lobos_v1,
aes(
x = K_corrected,
color = Diagnostic,
fill = Diagnostic,
alpha = 0.4
)) +
geom_density() +
geom_vline(data = cpK,
aes(
xintercept = optimal_cutpoint,
linetype = "dashed",
group = subgroup
)) +
geom_vline(data = cpK_MCI,
aes(
xintercept = optimal_cutpoint,
linetype = "dotted",
group = subgroup
)) +
theme_pubr() +
guides(alpha = "none", linetype = "none") +
theme(
axis.title = element_text(size = 11),
axis.text = element_text(size = 10),
text = element_text(size = 10)
) +
facet_grid(subgroup ~ .) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
# cutpoint_a
cutpoint_b_lobes <-
ggplot(
dados_lobos_v1,
aes(
x = logAvgThickness,
color = Diagnostic,
fill = Diagnostic,
alpha = 0.4
)
) +
geom_density() +
geom_vline(data = cpT,
aes(
xintercept = optimal_cutpoint,
linetype = "dashed",
group = subgroup
)) +
geom_vline(data = cpT_MCI,
aes(
xintercept = optimal_cutpoint,
linetype = "dotted",
group = subgroup
)) +
theme_pubr() +
guides(alpha = "none", linetype = "none") +
theme(
axis.title = element_text(size = 11),
axis.text = element_text(size = 10),
text = element_text(size = 10)
) + facet_grid(subgroup ~ .)
# cutpoint_b
fig_cutpoint_lobes <-
ggarrange(
cutpoint_a_lobes,
cutpoint_b_lobes,
labels = c("A", "B"),
ncol = 1,
font.label = list(size = 11),
common.legend = TRUE,
legend = "top"
) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
fig_cutpoint_lobes
11 Table 1
11.0.1 Girifcacao e envelhecimento
11.0.1.1 Reduzindo o efeito da idade
##
## Call:
## lm(formula = 1/2 * logAvgThickness_age_decay + logTotalArea_age_decay ~
## logExposedArea_age_decay, data = dados_hemi_v1, na.action = na.omit)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.032355 -0.008226 0.000773 0.008828 0.028420
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.41298 0.13258 3.115 0.00206 **
## logExposedArea_age_decay 1.05030 0.02873 36.554 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.01196 on 244 degrees of freedom
## Multiple R-squared: 0.8456, Adjusted R-squared: 0.845
## F-statistic: 1336 on 1 and 244 DF, p-value: < 2.2e-16| Diagnostic | term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|
| AD | (Intercept) | 0.42 | 0.37 | 1.14 | 0.26 | -0.34 | 1.18 |
| AD | logExposedArea_age_decay | 1.05 | 0.08 | 13.07 | 0.00 | 0.88 | 1.21 |
| MCI | (Intercept) | 0.61 | 0.20 | 3.05 | 0.00 | 0.21 | 1.01 |
| MCI | logExposedArea_age_decay | 1.01 | 0.04 | 23.28 | 0.00 | 0.92 | 1.09 |
| CTL | (Intercept) | 0.18 | 0.18 | 1.00 | 0.32 | -0.17 | 0.53 |
| CTL | logExposedArea_age_decay | 1.10 | 0.04 | 28.77 | 0.00 | 1.03 | 1.18 |
##
## Kruskal-Wallis rank sum test
##
## data: estimate by Diagnostic
## Kruskal-Wallis chi-squared = 2, df = 2, p-value = 0.3679
11.0.2 Diferenca entre diagnosticos
11.0.2.1 Cut point
11.0.2.1.1 Lobes deaged
cpK <-
cutpointr(
filter(dados_lobos_v1, Diagnostic == "AD" | Diagnostic == "CTL"),
K_age_decay,
Diagnostic,
ROI,
pos_class = "AD",
neg_class = "CTL",
method = maximize_boot_metric,
metric = sum_sens_spec,
na.rm = TRUE,
boot_runs = 1000,
use_midpoints = TRUE)
summary(cpK)## Method: maximize_boot_metric
## Predictor: K_age_decay
## Outcome: Diagnostic
## Direction: <=
## Subgroups: F, P, T, O
## Nr. of bootstraps: 1000
##
## Subgroup: F
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.6994 177 26 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## -0.4629 1.2695 0.7571 0.4615 0.8079 12 14 29 122
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu.
## Overall -0.4924472 -0.4777872 -0.4618779 -0.4493759 -0.4504447 -0.4382565
## AD -0.4924472 -0.4858975 -0.4771550 -0.4608831 -0.4613222 -0.4447041
## MCI NA NA NA NA NaN NA
## CTL -0.4889670 -0.4736635 -0.4585019 -0.4487091 -0.4485717 -0.4360336
## 95% Max. SD NAs
## -0.4272281 -0.4191747 0.01623795 0
## -0.4359569 -0.4332498 0.01813598 0
## NA NA NA 0
## -0.4264298 -0.4191747 0.01518272 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## optimal_cutpoint -0.48 -0.47 -0.47 -0.46 -0.46 -0.46 -0.45 -0.44 0.01
## AUC_b 0.49 0.60 0.66 0.70 0.70 0.74 0.79 0.87 0.06
## AUC_oob 0.46 0.57 0.65 0.71 0.70 0.75 0.83 0.94 0.08
## sum_sens_spec_b 0.79 1.10 1.22 1.30 1.29 1.37 1.47 1.62 0.11
## sum_sens_spec_oob 0.82 1.01 1.16 1.24 1.24 1.33 1.47 1.70 0.14
## acc_b 0.30 0.55 0.69 0.74 0.73 0.78 0.85 0.92 0.09
## acc_oob 0.40 0.55 0.67 0.73 0.72 0.78 0.84 0.90 0.09
## sensitivity_b 0.30 0.37 0.45 0.52 0.53 0.60 0.70 0.83 0.10
## sensitivity_oob 0.00 0.20 0.36 0.50 0.49 0.62 0.80 1.00 0.18
## specificity_b 0.24 0.55 0.72 0.78 0.77 0.83 0.92 0.97 0.11
## specificity_oob 0.27 0.52 0.69 0.77 0.76 0.84 0.92 0.98 0.12
## cohens_kappa_b -0.10 0.05 0.15 0.23 0.23 0.29 0.41 0.65 0.11
## cohens_kappa_oob -0.15 0.01 0.11 0.18 0.18 0.25 0.35 0.57 0.10
## NAs
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
##
## Subgroup: P
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.7858 177 26 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## -0.3375 1.4911 0.7288 0.7692 0.7219 20 6 42 109
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu.
## Overall -0.3887231 -0.3609292 -0.3417547 -0.3303306 -0.3324749 -0.3209917
## AD -0.3673445 -0.3645357 -0.3577182 -0.3451577 -0.3459457 -0.3386622
## MCI NA NA NA NA NaN NA
## CTL -0.3887231 -0.3595692 -0.3382488 -0.3285399 -0.3301554 -0.3194001
## 95% Max. SD NAs
## -0.3093390 -0.2967149 0.01692941 0
## -0.3260820 -0.3201238 0.01326828 0
## NA NA NA 0
## -0.3082498 -0.2967149 0.01643388 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## optimal_cutpoint -0.35 -0.34 -0.34 -0.34 -0.34 -0.34 -0.33 -0.33 0.00
## AUC_b 0.64 0.71 0.76 0.79 0.78 0.82 0.86 0.92 0.05
## AUC_oob 0.57 0.69 0.75 0.79 0.79 0.83 0.88 0.96 0.06
## sum_sens_spec_b 1.10 1.29 1.39 1.47 1.47 1.54 1.64 1.78 0.11
## sum_sens_spec_oob 0.92 1.17 1.34 1.44 1.43 1.54 1.66 1.77 0.15
## acc_b 0.49 0.62 0.69 0.73 0.73 0.77 0.81 0.89 0.06
## acc_oob 0.45 0.62 0.68 0.72 0.71 0.75 0.80 0.87 0.06
## sensitivity_b 0.47 0.61 0.69 0.74 0.74 0.81 0.88 0.96 0.09
## sensitivity_oob 0.10 0.38 0.60 0.75 0.71 0.85 1.00 1.00 0.18
## specificity_b 0.43 0.60 0.68 0.73 0.72 0.77 0.82 0.91 0.07
## specificity_oob 0.34 0.58 0.66 0.72 0.72 0.77 0.84 0.96 0.08
## cohens_kappa_b 0.04 0.15 0.24 0.31 0.30 0.37 0.45 0.60 0.09
## cohens_kappa_oob -0.08 0.11 0.20 0.27 0.27 0.34 0.44 0.60 0.10
## NAs
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
##
## Subgroup: T
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.7504 177 26 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## -0.3393 1.3214 0.6384 0.6923 0.6291 18 8 56 95
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu.
## Overall -0.3862226 -0.3693890 -0.3449413 -0.3362110 -0.3372773 -0.3269770
## AD -0.3862226 -0.3793668 -0.3651671 -0.3476139 -0.3513326 -0.3366410
## MCI NA NA NA NA NaN NA
## CTL -0.3723159 -0.3605893 -0.3441472 -0.3347947 -0.3348572 -0.3258725
## 95% Max. SD NAs
## -0.3115348 -0.2980570 0.01646089 0
## -0.3310313 -0.3219632 0.01803107 0
## NA NA NA 0
## -0.3109501 -0.2980570 0.01495334 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## optimal_cutpoint -0.36 -0.35 -0.35 -0.34 -0.34 -0.34 -0.33 -0.33 0.01
## AUC_b 0.54 0.66 0.71 0.75 0.75 0.79 0.83 0.89 0.05
## AUC_oob 0.46 0.64 0.71 0.76 0.76 0.81 0.87 0.99 0.07
## sum_sens_spec_b 0.98 1.16 1.27 1.34 1.34 1.42 1.52 1.67 0.11
## sum_sens_spec_oob 0.80 1.04 1.21 1.30 1.29 1.38 1.52 1.74 0.14
## acc_b 0.44 0.53 0.63 0.69 0.69 0.77 0.84 0.92 0.09
## acc_oob 0.41 0.51 0.61 0.68 0.68 0.75 0.82 0.90 0.10
## sensitivity_b 0.30 0.46 0.56 0.64 0.64 0.72 0.82 0.97 0.11
## sensitivity_oob 0.00 0.25 0.45 0.62 0.60 0.75 0.90 1.00 0.20
## specificity_b 0.38 0.49 0.62 0.70 0.70 0.80 0.88 0.95 0.12
## specificity_oob 0.33 0.46 0.60 0.69 0.69 0.80 0.90 0.96 0.13
## cohens_kappa_b -0.01 0.08 0.15 0.22 0.23 0.29 0.41 0.62 0.10
## cohens_kappa_oob -0.17 0.03 0.12 0.18 0.19 0.25 0.35 0.57 0.10
## NAs
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
##
## Subgroup: O
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.6694 177 26 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## -0.3531 1.2417 0.7062 0.5 0.7417 13 13 39 112
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu.
## Overall -0.4071304 -0.3816251 -0.3556343 -0.3413318 -0.3412010 -0.3271589
## AD -0.4071304 -0.3888987 -0.3758057 -0.3538700 -0.3547200 -0.3347385
## MCI NA NA NA NA NaN NA
## CTL -0.3914546 -0.3758727 -0.3531679 -0.3392267 -0.3388732 -0.3262021
## 95% Max. SD NAs
## -0.2999387 -0.2784499 0.02333062 0
## -0.3130321 -0.3074425 0.02669722 0
## NA NA NA 0
## -0.2976979 -0.2784499 0.02197141 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## optimal_cutpoint -0.38 -0.37 -0.36 -0.35 -0.35 -0.35 -0.35 -0.33 0.01
## AUC_b 0.43 0.57 0.63 0.67 0.67 0.71 0.78 0.87 0.06
## AUC_oob 0.38 0.52 0.61 0.67 0.67 0.73 0.81 0.97 0.09
## sum_sens_spec_b 0.94 1.10 1.22 1.29 1.29 1.36 1.47 1.67 0.11
## sum_sens_spec_oob 0.80 0.99 1.13 1.22 1.23 1.33 1.47 1.82 0.15
## acc_b 0.37 0.62 0.68 0.73 0.73 0.78 0.84 0.90 0.07
## acc_oob 0.44 0.59 0.67 0.72 0.72 0.77 0.84 0.91 0.08
## sensitivity_b 0.19 0.32 0.43 0.52 0.52 0.61 0.73 0.89 0.12
## sensitivity_oob 0.00 0.18 0.33 0.46 0.47 0.60 0.75 1.00 0.18
## specificity_b 0.32 0.62 0.70 0.77 0.77 0.84 0.91 0.98 0.09
## specificity_oob 0.38 0.60 0.69 0.76 0.76 0.84 0.92 0.98 0.10
## cohens_kappa_b -0.04 0.07 0.15 0.22 0.22 0.28 0.38 0.53 0.09
## cohens_kappa_oob -0.17 0.00 0.10 0.16 0.17 0.24 0.36 0.52 0.11
## NAs
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0plot(cpK)
cpK_MCI <-
cutpointr(
filter(dados_lobos_v1, Diagnostic == "MCI" | Diagnostic == "CTL"),
K_age_decay,
Diagnostic,
ROI,
pos_class = "MCI",
neg_class = "CTL",
method = maximize_boot_metric,
metric = sum_sens_spec,
na.rm = TRUE,
boot_runs = 1000,
use_midpoints = TRUE)
summary(cpK_MCI)## Method: maximize_boot_metric
## Predictor: K_age_decay
## Outcome: Diagnostic
## Direction: <=
## Subgroups: F, P, T, O
## Nr. of bootstraps: 1000
##
## Subgroup: F
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.5416 216 65 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## -0.4496 1.0067 0.5139 0.4769 0.5298 31 34 71 80
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu.
## Overall -0.4889670 -0.4757776 -0.4597265 -0.4485104 -0.4492211 -0.4378026
## AD NA NA NA NA NaN NA
## MCI -0.4830813 -0.4761680 -0.4609377 -0.4482128 -0.4507296 -0.4401716
## CTL -0.4889670 -0.4736635 -0.4585019 -0.4487091 -0.4485717 -0.4360336
## 95% Max. SD NAs
## -0.4267766 -0.4173079 0.01535924 0
## NA NA NA 0
## -0.4272361 -0.4173079 0.01577727 0
## -0.4264298 -0.4191747 0.01518272 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## optimal_cutpoint -0.47 -0.46 -0.45 -0.45 -0.45 -0.45 -0.44 -0.43 0.01
## AUC_b 0.38 0.47 0.51 0.54 0.54 0.57 0.61 0.69 0.04
## AUC_oob 0.37 0.45 0.50 0.54 0.54 0.58 0.64 0.70 0.06
## sum_sens_spec_b 0.68 0.94 1.02 1.07 1.07 1.13 1.19 1.30 0.08
## sum_sens_spec_oob 0.74 0.86 0.96 1.03 1.02 1.09 1.19 1.35 0.10
## acc_b 0.36 0.44 0.49 0.54 0.54 0.60 0.65 0.74 0.07
## acc_oob 0.30 0.41 0.47 0.52 0.53 0.58 0.65 0.72 0.07
## sensitivity_b 0.11 0.25 0.42 0.51 0.51 0.61 0.77 0.89 0.15
## sensitivity_oob 0.00 0.18 0.36 0.48 0.48 0.60 0.75 0.95 0.17
## specificity_b 0.18 0.34 0.45 0.55 0.56 0.67 0.80 0.94 0.15
## specificity_oob 0.17 0.30 0.43 0.54 0.54 0.65 0.80 0.95 0.15
## cohens_kappa_b -0.27 -0.05 0.02 0.06 0.06 0.11 0.18 0.26 0.07
## cohens_kappa_oob -0.25 -0.12 -0.04 0.02 0.02 0.08 0.16 0.33 0.09
## NAs
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
##
## Subgroup: P
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.4746 216 65 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## -0.3438 1.0827 0.6528 0.2615 0.8212 17 48 27 124
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu.
## Overall -0.3887231 -0.3615902 -0.3400921 -0.3280799 -0.3298689 -0.3182982
## AD NA NA NA NA NaN NA
## MCI -0.3733775 -0.3624233 -0.3442249 -0.3234164 -0.3292033 -0.3144146
## CTL -0.3887231 -0.3595692 -0.3382488 -0.3285399 -0.3301554 -0.3194001
## 95% Max. SD NAs
## -0.3064748 -0.2866583 0.01754076 0
## NA NA NA 0
## -0.3037192 -0.2866583 0.01999951 0
## -0.3082498 -0.2967149 0.01643388 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## optimal_cutpoint -0.36 -0.35 -0.34 -0.34 -0.34 -0.34 -0.33 -0.31 0.00
## AUC_b 0.33 0.40 0.44 0.47 0.47 0.51 0.55 0.62 0.05
## AUC_oob 0.27 0.37 0.43 0.48 0.48 0.52 0.58 0.70 0.06
## sum_sens_spec_b 0.70 0.96 1.05 1.09 1.09 1.14 1.21 1.29 0.08
## sum_sens_spec_oob 0.72 0.92 1.00 1.06 1.06 1.12 1.20 1.36 0.09
## acc_b 0.30 0.54 0.62 0.65 0.64 0.68 0.71 0.76 0.06
## acc_oob 0.31 0.53 0.60 0.64 0.63 0.67 0.71 0.78 0.06
## sensitivity_b 0.11 0.18 0.24 0.29 0.30 0.35 0.42 0.84 0.08
## sensitivity_oob 0.00 0.12 0.20 0.27 0.28 0.33 0.45 0.84 0.11
## specificity_b 0.12 0.63 0.77 0.81 0.79 0.85 0.91 0.96 0.09
## specificity_oob 0.11 0.64 0.75 0.80 0.79 0.85 0.90 0.96 0.10
## cohens_kappa_b -0.24 -0.04 0.05 0.10 0.10 0.15 0.22 0.31 0.08
## cohens_kappa_oob -0.23 -0.08 0.01 0.06 0.07 0.13 0.22 0.38 0.09
## NAs
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
##
## Subgroup: T
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.6057 216 65 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## -0.3473 1.1904 0.6852 0.3692 0.8212 24 41 27 124
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu.
## Overall -0.3801095 -0.3621463 -0.3458489 -0.3363093 -0.3368114 -0.3262343
## AD NA NA NA NA NaN NA
## MCI -0.3801095 -0.3674622 -0.3556756 -0.3423442 -0.3413513 -0.3265369
## CTL -0.3723159 -0.3605893 -0.3441472 -0.3347947 -0.3348572 -0.3258725
## 95% Max. SD NAs
## -0.3112199 -0.2980570 0.01598388 0
## NA NA NA 0
## -0.3135545 -0.3066956 0.01744295 0
## -0.3109501 -0.2980570 0.01495334 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## optimal_cutpoint -0.36 -0.35 -0.35 -0.35 -0.35 -0.34 -0.34 -0.33 0.00
## AUC_b 0.47 0.54 0.58 0.61 0.61 0.64 0.67 0.74 0.04
## AUC_oob 0.43 0.51 0.57 0.61 0.61 0.65 0.70 0.80 0.06
## sum_sens_spec_b 1.00 1.09 1.17 1.22 1.21 1.26 1.33 1.45 0.07
## sum_sens_spec_oob 0.88 1.03 1.11 1.18 1.18 1.24 1.33 1.48 0.09
## acc_b 0.48 0.59 0.64 0.68 0.67 0.71 0.75 0.79 0.05
## acc_oob 0.42 0.56 0.62 0.66 0.66 0.70 0.75 0.81 0.06
## sensitivity_b 0.22 0.31 0.38 0.43 0.44 0.49 0.58 0.74 0.08
## sensitivity_oob 0.05 0.25 0.33 0.41 0.41 0.48 0.60 0.84 0.11
## specificity_b 0.38 0.63 0.72 0.79 0.78 0.84 0.88 0.95 0.08
## specificity_oob 0.29 0.57 0.70 0.79 0.76 0.84 0.89 0.95 0.10
## cohens_kappa_b 0.00 0.08 0.16 0.22 0.22 0.27 0.34 0.49 0.08
## cohens_kappa_oob -0.12 0.03 0.11 0.18 0.18 0.24 0.33 0.45 0.09
## NAs
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
##
## Subgroup: O
## --------------------------------------------------------------------------------
## AUC n n_pos n_neg
## 0.5627 216 65 151
##
## optimal_cutpoint sum_sens_spec acc sensitivity specificity tp fn fp tn
## -0.3343 1.1027 0.4769 0.7385 0.3642 48 17 96 55
##
## Predictor summary:
## Data Min. 5% 1st Qu. Median Mean 3rd Qu.
## Overall -0.3914546 -0.3737629 -0.3531416 -0.3404060 -0.3401636 -0.3291942
## AD NA NA NA NA NaN NA
## MCI -0.3738751 -0.3664919 -0.3525155 -0.3446323 -0.3431611 -0.3340610
## CTL -0.3914546 -0.3758727 -0.3531679 -0.3392267 -0.3388732 -0.3262021
## 95% Max. SD NAs
## -0.3074186 -0.2784499 0.02028191 0
## NA NA NA 0
## -0.3133603 -0.3085133 0.01540832 0
## -0.2976979 -0.2784499 0.02197141 0
##
## Bootstrap summary:
## Variable Min. 5% 1st Qu. Median Mean 3rd Qu. 95% Max. SD
## optimal_cutpoint -0.35 -0.34 -0.34 -0.34 -0.33 -0.33 -0.33 -0.32 0.01
## AUC_b 0.42 0.50 0.53 0.56 0.56 0.59 0.63 0.67 0.04
## AUC_oob 0.40 0.47 0.52 0.56 0.56 0.60 0.65 0.76 0.06
## sum_sens_spec_b 0.93 1.03 1.09 1.14 1.14 1.19 1.26 1.37 0.07
## sum_sens_spec_oob 0.78 0.94 1.03 1.09 1.09 1.16 1.24 1.39 0.09
## acc_b 0.31 0.41 0.46 0.50 0.51 0.56 0.61 0.67 0.06
## acc_oob 0.32 0.40 0.45 0.49 0.49 0.53 0.58 0.65 0.05
## sensitivity_b 0.42 0.57 0.66 0.73 0.72 0.79 0.87 0.93 0.09
## sensitivity_oob 0.25 0.44 0.58 0.70 0.69 0.82 0.92 1.00 0.15
## specificity_b 0.13 0.23 0.32 0.41 0.42 0.50 0.61 0.71 0.12
## specificity_oob 0.11 0.22 0.31 0.40 0.40 0.48 0.58 0.73 0.12
## cohens_kappa_b -0.05 0.02 0.07 0.10 0.11 0.15 0.21 0.31 0.06
## cohens_kappa_oob -0.20 -0.05 0.02 0.07 0.07 0.12 0.18 0.26 0.07
## NAs
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0
## 0plot(cpK_MCI)
11.0.2.2 LM
| Diagnostic | term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|
| AD | (Intercept) | 0.20 | 0.39 | 0.50 | 0.62 | -0.62 | 1.01 |
| AD | logExposedArea | 1.09 | 0.09 | 12.60 | 0.00 | 0.91 | 1.27 |
| MCI | (Intercept) | 0.53 | 0.21 | 2.50 | 0.02 | 0.11 | 0.95 |
| MCI | logExposedArea | 1.02 | 0.05 | 22.08 | 0.00 | 0.93 | 1.11 |
| CTL | (Intercept) | -0.23 | 0.18 | -1.27 | 0.21 | -0.60 | 0.13 |
| CTL | logExposedArea | 1.19 | 0.04 | 29.52 | 0.00 | 1.11 | 1.27 |
## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.058 0.0292 81.596 <2e-16 ***
## ROI 4 6.222 1.5556 4347.166 <2e-16 ***
## Diagnostic:ROI 8 0.003 0.0004 1.013 0.424
## Residuals 1207 0.432 0.0004
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
ggplot(dados_hemi_v1, aes(x = Diagnostic, y = S, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
geom_boxplot() +
theme_pubr() +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
ggplot(dados_hemi_v1, aes(x= S, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
geom_density() +
theme_pubr() +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
ggbetweenstats(
data = dados_hemi_v1,
x = Diagnostic,
y = S, outlier.tagging = TRUE,
plot.type = "box"
) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
aov_diag <- aov(S ~ Diagnostic, data = dados_hemi_v1)
summary(aov_diag)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.1539 0.07697 6.275 0.0022 **
## Residuals 243 2.9810 0.01227
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1aov_diag_K_diag_TK <- TukeyHSD(aov_diag)
aov_diag_K_diag_TK## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = S ~ Diagnostic, data = dados_hemi_v1)
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD -0.04832085 -0.10879738 0.012155678 0.1454395
## CTL-AD -0.07844671 -0.13382516 -0.023068259 0.0027747
## CTL-MCI -0.03012586 -0.06855234 0.008300619 0.1561058ggplot(dados_hemi_v1, aes(x = Diagnostic, y = I, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
geom_boxplot() +
theme_pubr() +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
ggplot(dados_hemi_v1, aes(x= I, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
geom_density() +
theme_pubr() +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
ggbetweenstats(
data = dados_hemi_v1,
x = Diagnostic,
y = I, outlier.tagging = TRUE,
plot.type = "box"
) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
aov_diag <- aov(I ~ Diagnostic, data = dados_hemi_v1)
summary(aov_diag)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.1083 0.05415 11.21 2.2e-05 ***
## Residuals 243 1.1734 0.00483
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1aov_diag_K_diag_TK <- TukeyHSD(aov_diag)
aov_diag_K_diag_TK## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = I ~ Diagnostic, data = dados_hemi_v1)
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.04158669 0.003644613 0.07952877 0.0277853
## CTL-AD 0.06616956 0.031425940 0.10091318 0.0000325
## CTL-MCI 0.02458287 0.000474665 0.04869107 0.0445049Is it easier to diff diag when younger?
ggplot(filter(dados_hemi_v1, Age_interval != "[45,50)"& Age_interval != "[50,55)"& Age_interval != "[40,45)"& Age_interval != "[55,60)"), aes(x = Age_interval, y = logAvgThickness, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
geom_boxplot() +
theme_pubr() +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
aov_diag2 <- aov(logAvgThickness ~ Diagnostic*Age_interval, data = filter(dados_hemi_v1, Age_interval != "[45,50)"& Age_interval != "[50,55)"& Age_interval != "[40,45)"& Age_interval != "[55,60)"))
summary(aov_diag2)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00951 0.004754 24.206 3.90e-10 ***
## Age_interval 5 0.01008 0.002016 10.266 9.08e-09 ***
## Diagnostic:Age_interval 7 0.00581 0.000830 4.224 0.000228 ***
## Residuals 199 0.03909 0.000196
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1aov_diag_2_diag_TK <- TukeyHSD(aov_diag2)
aov_diag_2_diag_TK$`Diagnostic:Age_interval`## diff lwr upr p adj
## MCI:[60,65)-AD:[60,65) 0.0247295988 -0.024817696 0.0742768933 9.504734e-01
## CTL:[60,65)-AD:[60,65) 0.0440618281 0.007948342 0.0801753144 3.252613e-03
## AD:[65,70)-AD:[60,65) NA NA NA NA
## MCI:[65,70)-AD:[60,65) 0.0392822521 0.002536995 0.0760275091 2.283589e-02
## CTL:[65,70)-AD:[60,65) 0.0430458757 0.007050601 0.0790411506 4.534547e-03
## AD:[70,75)-AD:[60,65) 0.0202524211 -0.020202775 0.0607076177 9.491546e-01
## MCI:[70,75)-AD:[60,65) 0.0308792707 -0.005478504 0.0672370450 2.092742e-01
## CTL:[70,75)-AD:[60,65) 0.0228814145 -0.013169538 0.0589323666 7.217380e-01
## AD:[75,80)-AD:[60,65) 0.0179841586 -0.020395011 0.0563633279 9.721875e-01
## MCI:[75,80)-AD:[60,65) 0.0226882735 -0.015154098 0.0605306448 8.015808e-01
## CTL:[75,80)-AD:[60,65) 0.0332570904 -0.003673282 0.0701874633 1.347703e-01
## AD:[80,85)-AD:[60,65) 0.0183866342 -0.022068562 0.0588418308 9.793185e-01
## MCI:[80,85)-AD:[60,65) 0.0117412129 -0.028713984 0.0521964094 9.999045e-01
## CTL:[80,85)-AD:[60,65) 0.0383324981 -0.011214796 0.0878797926 3.636968e-01
## AD:[85,90)-AD:[60,65) -0.0052373330 -0.054784627 0.0443099615 1.000000e+00
## MCI:[85,90)-AD:[60,65) NA NA NA NA
## CTL:[85,90)-AD:[60,65) NA NA NA NA
## CTL:[60,65)-MCI:[60,65) 0.0193322293 -0.016781257 0.0554457156 9.113170e-01
## AD:[65,70)-MCI:[60,65) NA NA NA NA
## MCI:[65,70)-MCI:[60,65) 0.0145526533 -0.022192604 0.0512979103 9.952006e-01
## CTL:[65,70)-MCI:[60,65) 0.0183162769 -0.017678998 0.0543115518 9.414707e-01
## AD:[70,75)-MCI:[60,65) -0.0044771777 -0.044932374 0.0359780189 1.000000e+00
## MCI:[70,75)-MCI:[60,65) 0.0061496719 -0.030208102 0.0425074462 1.000000e+00
## CTL:[70,75)-MCI:[60,65) -0.0018481843 -0.037899136 0.0342027678 1.000000e+00
## AD:[75,80)-MCI:[60,65) -0.0067454402 -0.045124609 0.0316337291 9.999999e-01
## MCI:[75,80)-MCI:[60,65) -0.0020413253 -0.039883697 0.0358010460 1.000000e+00
## CTL:[75,80)-MCI:[60,65) 0.0085274916 -0.028402881 0.0454578645 9.999964e-01
## AD:[80,85)-MCI:[60,65) -0.0063429646 -0.046798161 0.0341122320 1.000000e+00
## MCI:[80,85)-MCI:[60,65) -0.0129883859 -0.053443582 0.0274668106 9.996311e-01
## CTL:[80,85)-MCI:[60,65) 0.0136028994 -0.035944395 0.0631501939 9.999558e-01
## AD:[85,90)-MCI:[60,65) -0.0299669318 -0.079514226 0.0195803627 7.903899e-01
## MCI:[85,90)-MCI:[60,65) NA NA NA NA
## CTL:[85,90)-MCI:[60,65) NA NA NA NA
## AD:[65,70)-CTL:[60,65) NA NA NA NA
## MCI:[65,70)-CTL:[60,65) -0.0047795760 -0.018902728 0.0093435759 9.992738e-01
## CTL:[65,70)-CTL:[60,65) -0.0010159524 -0.013053781 0.0110218764 1.000000e+00
## AD:[70,75)-CTL:[60,65) -0.0238094069 -0.045851921 -0.0017668927 1.999484e-02
## MCI:[70,75)-CTL:[60,65) -0.0131825573 -0.026264501 -0.0001006135 4.598849e-02
## CTL:[70,75)-CTL:[60,65) -0.0211804136 -0.033383719 -0.0089771086 6.768297e-07
## AD:[75,80)-CTL:[60,65) -0.0260776695 -0.044027882 -0.0081274567 9.597817e-05
## MCI:[75,80)-CTL:[60,65) -0.0213735546 -0.038145393 -0.0046017161 1.506187e-03
## CTL:[75,80)-CTL:[60,65) -0.0108047376 -0.025402749 0.0037932740 4.465391e-01
## AD:[80,85)-CTL:[60,65) -0.0256751938 -0.047717708 -0.0036326796 6.871740e-03
## MCI:[80,85)-CTL:[60,65) -0.0323206152 -0.054363129 -0.0102781010 7.693327e-05
## CTL:[80,85)-CTL:[60,65) -0.0057293299 -0.041842816 0.0303841564 1.000000e+00
## AD:[85,90)-CTL:[60,65) -0.0492991610 -0.085412647 -0.0131856747 3.865864e-04
## MCI:[85,90)-CTL:[60,65) NA NA NA NA
## CTL:[85,90)-CTL:[60,65) NA NA NA NA
## MCI:[65,70)-AD:[65,70) NA NA NA NA
## CTL:[65,70)-AD:[65,70) NA NA NA NA
## AD:[70,75)-AD:[65,70) NA NA NA NA
## MCI:[70,75)-AD:[65,70) NA NA NA NA
## CTL:[70,75)-AD:[65,70) NA NA NA NA
## AD:[75,80)-AD:[65,70) NA NA NA NA
## MCI:[75,80)-AD:[65,70) NA NA NA NA
## CTL:[75,80)-AD:[65,70) NA NA NA NA
## AD:[80,85)-AD:[65,70) NA NA NA NA
## MCI:[80,85)-AD:[65,70) NA NA NA NA
## CTL:[80,85)-AD:[65,70) NA NA NA NA
## AD:[85,90)-AD:[65,70) NA NA NA NA
## MCI:[85,90)-AD:[65,70) NA NA NA NA
## CTL:[85,90)-AD:[65,70) NA NA NA NA
## CTL:[65,70)-MCI:[65,70) 0.0037636236 -0.010054457 0.0175817039 9.999605e-01
## AD:[70,75)-MCI:[65,70) -0.0190298309 -0.042092841 0.0040331795 2.530061e-01
## MCI:[70,75)-MCI:[65,70) -0.0084029813 -0.023139578 0.0063336151 8.580698e-01
## CTL:[70,75)-MCI:[65,70) -0.0164008376 -0.030363311 -0.0024383639 6.032595e-03
## AD:[75,80)-MCI:[65,70) -0.0212980935 -0.040487678 -0.0021085088 1.389610e-02
## MCI:[75,80)-MCI:[65,70) -0.0165939785 -0.034686092 0.0014981354 1.155479e-01
## CTL:[75,80)-MCI:[65,70) -0.0060251616 -0.022122738 0.0100724147 9.975039e-01
## AD:[80,85)-MCI:[65,70) -0.0208956178 -0.043958628 0.0021673926 1.281810e-01
## MCI:[80,85)-MCI:[65,70) -0.0275410392 -0.050604050 -0.0044780287 4.640981e-03
## CTL:[80,85)-MCI:[65,70) -0.0009497539 -0.037695011 0.0357955031 1.000000e+00
## AD:[85,90)-MCI:[65,70) -0.0445195850 -0.081264842 -0.0077743280 3.658894e-03
## MCI:[85,90)-MCI:[65,70) NA NA NA NA
## CTL:[85,90)-MCI:[65,70) NA NA NA NA
## AD:[70,75)-CTL:[65,70) -0.0227934545 -0.044641758 -0.0009451513 3.086157e-02
## MCI:[70,75)-CTL:[65,70) -0.0121666049 -0.024918592 0.0005853822 8.071838e-02
## CTL:[70,75)-CTL:[65,70) -0.0201644612 -0.032013367 -0.0083155552 1.265567e-06
## AD:[75,80)-CTL:[65,70) -0.0250617171 -0.042772902 -0.0073505324 1.764978e-04
## MCI:[75,80)-CTL:[65,70) -0.0203576022 -0.036873367 -0.0038418373 2.731578e-03
## CTL:[75,80)-CTL:[65,70) -0.0097887852 -0.024091857 0.0045142867 5.940247e-01
## AD:[80,85)-CTL:[65,70) -0.0246592414 -0.046507545 -0.0028109382 1.097228e-02
## MCI:[80,85)-CTL:[65,70) -0.0313046628 -0.053152966 -0.0094563596 1.326660e-04
## CTL:[80,85)-CTL:[65,70) -0.0047133775 -0.040708652 0.0312818974 1.000000e+00
## AD:[85,90)-CTL:[65,70) -0.0482832086 -0.084278484 -0.0122879337 5.564458e-04
## MCI:[85,90)-CTL:[65,70) NA NA NA NA
## CTL:[85,90)-CTL:[65,70) NA NA NA NA
## MCI:[70,75)-AD:[70,75) 0.0106268496 -0.011813656 0.0330673551 9.692717e-01
## CTL:[70,75)-AD:[70,75) 0.0026289933 -0.019310917 0.0245689041 1.000000e+00
## AD:[75,80)-AD:[70,75) -0.0022682625 -0.027854375 0.0233178503 1.000000e+00
## MCI:[75,80)-AD:[70,75) 0.0024358524 -0.022337795 0.0272094996 1.000000e+00
## CTL:[75,80)-AD:[70,75) 0.0130046693 -0.010352149 0.0363614879 8.804043e-01
## AD:[80,85)-AD:[70,75) -0.0018657869 -0.030471931 0.0267403569 1.000000e+00
## MCI:[80,85)-AD:[70,75) -0.0085112083 -0.037117352 0.0200949355 9.998660e-01
## CTL:[80,85)-AD:[70,75) 0.0180800770 -0.022375120 0.0585352736 9.825094e-01
## AD:[85,90)-AD:[70,75) -0.0254897541 -0.065944951 0.0149654424 7.328777e-01
## MCI:[85,90)-AD:[70,75) NA NA NA NA
## CTL:[85,90)-AD:[70,75) NA NA NA NA
## CTL:[70,75)-MCI:[70,75) -0.0079978563 -0.020906168 0.0049104552 7.574760e-01
## AD:[75,80)-MCI:[70,75) -0.0128951121 -0.031331869 0.0055416444 5.538704e-01
## MCI:[75,80)-MCI:[70,75) -0.0081909972 -0.025482568 0.0091005735 9.691848e-01
## CTL:[75,80)-MCI:[70,75) 0.0023778197 -0.012814474 0.0175701137 1.000000e+00
## AD:[80,85)-MCI:[70,75) -0.0124926365 -0.034933142 0.0099478690 8.805340e-01
## MCI:[80,85)-MCI:[70,75) -0.0191380579 -0.041578563 0.0033024476 2.033763e-01
## CTL:[80,85)-MCI:[70,75) 0.0074532274 -0.028904547 0.0438110017 9.999994e-01
## AD:[85,90)-MCI:[70,75) -0.0361166037 -0.072474378 0.0002411705 5.369859e-02
## MCI:[85,90)-MCI:[70,75) NA NA NA NA
## CTL:[85,90)-MCI:[70,75) NA NA NA NA
## AD:[75,80)-CTL:[70,75) -0.0048972559 -0.022721324 0.0129268119 9.999553e-01
## MCI:[75,80)-CTL:[70,75) -0.0001931409 -0.016829902 0.0164436201 1.000000e+00
## CTL:[75,80)-CTL:[70,75) 0.0103756760 -0.004066941 0.0248182933 5.033192e-01
## AD:[80,85)-CTL:[70,75) -0.0044947802 -0.026434691 0.0174451306 9.999994e-01
## MCI:[80,85)-CTL:[70,75) -0.0111402016 -0.033080112 0.0107997092 9.425345e-01
## CTL:[80,85)-CTL:[70,75) 0.0154510837 -0.020599868 0.0515020358 9.886314e-01
## AD:[85,90)-CTL:[70,75) -0.0281187474 -0.064169700 0.0079322047 3.489203e-01
## MCI:[85,90)-CTL:[70,75) NA NA NA NA
## CTL:[85,90)-CTL:[70,75) NA NA NA NA
## MCI:[75,80)-AD:[75,80) 0.0047041149 -0.016510769 0.0259189990 9.999980e-01
## CTL:[75,80)-AD:[75,80) 0.0152729318 -0.004268785 0.0348146483 3.452659e-01
## AD:[80,85)-AD:[75,80) 0.0004024756 -0.025183637 0.0259885885 1.000000e+00
## MCI:[80,85)-AD:[75,80) -0.0062429457 -0.031829059 0.0193431671 9.999918e-01
## CTL:[80,85)-AD:[75,80) 0.0203483395 -0.018030830 0.0587275088 9.178325e-01
## AD:[85,90)-AD:[75,80) -0.0232214916 -0.061600661 0.0151576777 7.898732e-01
## MCI:[85,90)-AD:[75,80) NA NA NA NA
## CTL:[85,90)-AD:[75,80) NA NA NA NA
## CTL:[75,80)-MCI:[75,80) 0.0105688169 -0.007896370 0.0290340033 8.542780e-01
## AD:[80,85)-MCI:[75,80) -0.0043016393 -0.029075287 0.0204720080 1.000000e+00
## MCI:[80,85)-MCI:[75,80) -0.0109470607 -0.035720708 0.0138265866 9.844039e-01
## CTL:[80,85)-MCI:[75,80) 0.0156442246 -0.022198147 0.0534865959 9.922725e-01
## AD:[85,90)-MCI:[75,80) -0.0279256065 -0.065767978 0.0099167648 4.522147e-01
## MCI:[85,90)-MCI:[75,80) NA NA NA NA
## CTL:[85,90)-MCI:[75,80) NA NA NA NA
## AD:[80,85)-CTL:[75,80) -0.0148704562 -0.038227275 0.0084863624 7.169454e-01
## MCI:[80,85)-CTL:[75,80) -0.0215158776 -0.044872696 0.0018409411 1.112848e-01
## CTL:[80,85)-CTL:[75,80) 0.0050754077 -0.031854965 0.0420057806 1.000000e+00
## AD:[85,90)-CTL:[75,80) -0.0384944234 -0.075424796 -0.0015640505 3.118513e-02
## MCI:[85,90)-CTL:[75,80) NA NA NA NA
## CTL:[85,90)-CTL:[75,80) NA NA NA NA
## MCI:[80,85)-AD:[80,85) -0.0066454214 -0.035251565 0.0219607224 9.999960e-01
## CTL:[80,85)-AD:[80,85) 0.0199458639 -0.020509333 0.0604010605 9.555534e-01
## AD:[85,90)-AD:[80,85) -0.0236239672 -0.064079164 0.0168312293 8.328823e-01
## MCI:[85,90)-AD:[80,85) NA NA NA NA
## CTL:[85,90)-AD:[80,85) NA NA NA NA
## CTL:[80,85)-MCI:[80,85) 0.0265912853 -0.013863911 0.0670464818 6.651102e-01
## AD:[85,90)-MCI:[80,85) -0.0169785458 -0.057433742 0.0234766507 9.909039e-01
## MCI:[85,90)-MCI:[80,85) NA NA NA NA
## CTL:[85,90)-MCI:[80,85) NA NA NA NA
## AD:[85,90)-CTL:[80,85) -0.0435698311 -0.093117126 0.0059774634 1.626629e-01
## MCI:[85,90)-CTL:[80,85) NA NA NA NA
## CTL:[85,90)-CTL:[80,85) NA NA NA NA
## MCI:[85,90)-AD:[85,90) NA NA NA NA
## CTL:[85,90)-AD:[85,90) NA NA NA NA
## CTL:[85,90)-MCI:[85,90) NA NA NA NAggplot(filter(dados_hemi_v1, Age_interval != "[45,50)"& Age_interval != "[50,55)"& Age_interval != "[40,45)"& Age_interval != "[55,60)"), aes(x = Age_interval, y = K, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
geom_boxplot() +
theme_pubr() +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
aov_diag2 <- aov(logAvgThickness ~ Diagnostic*Age_interval, data = filter(dados_hemi_v1, Age_interval != "[45,50)"& Age_interval != "[50,55)"& Age_interval != "[40,45)"& Age_interval != "[55,60)"))
summary(aov_diag2)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00951 0.004754 24.206 3.90e-10 ***
## Age_interval 5 0.01008 0.002016 10.266 9.08e-09 ***
## Diagnostic:Age_interval 7 0.00581 0.000830 4.224 0.000228 ***
## Residuals 199 0.03909 0.000196
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1aov_diag_2_diag_TK <- TukeyHSD(aov_diag2)
aov_diag_2_diag_TK$`Diagnostic:Age_interval`## diff lwr upr p adj
## MCI:[60,65)-AD:[60,65) 0.0247295988 -0.024817696 0.0742768933 9.504734e-01
## CTL:[60,65)-AD:[60,65) 0.0440618281 0.007948342 0.0801753144 3.252613e-03
## AD:[65,70)-AD:[60,65) NA NA NA NA
## MCI:[65,70)-AD:[60,65) 0.0392822521 0.002536995 0.0760275091 2.283589e-02
## CTL:[65,70)-AD:[60,65) 0.0430458757 0.007050601 0.0790411506 4.534547e-03
## AD:[70,75)-AD:[60,65) 0.0202524211 -0.020202775 0.0607076177 9.491546e-01
## MCI:[70,75)-AD:[60,65) 0.0308792707 -0.005478504 0.0672370450 2.092742e-01
## CTL:[70,75)-AD:[60,65) 0.0228814145 -0.013169538 0.0589323666 7.217380e-01
## AD:[75,80)-AD:[60,65) 0.0179841586 -0.020395011 0.0563633279 9.721875e-01
## MCI:[75,80)-AD:[60,65) 0.0226882735 -0.015154098 0.0605306448 8.015808e-01
## CTL:[75,80)-AD:[60,65) 0.0332570904 -0.003673282 0.0701874633 1.347703e-01
## AD:[80,85)-AD:[60,65) 0.0183866342 -0.022068562 0.0588418308 9.793185e-01
## MCI:[80,85)-AD:[60,65) 0.0117412129 -0.028713984 0.0521964094 9.999045e-01
## CTL:[80,85)-AD:[60,65) 0.0383324981 -0.011214796 0.0878797926 3.636968e-01
## AD:[85,90)-AD:[60,65) -0.0052373330 -0.054784627 0.0443099615 1.000000e+00
## MCI:[85,90)-AD:[60,65) NA NA NA NA
## CTL:[85,90)-AD:[60,65) NA NA NA NA
## CTL:[60,65)-MCI:[60,65) 0.0193322293 -0.016781257 0.0554457156 9.113170e-01
## AD:[65,70)-MCI:[60,65) NA NA NA NA
## MCI:[65,70)-MCI:[60,65) 0.0145526533 -0.022192604 0.0512979103 9.952006e-01
## CTL:[65,70)-MCI:[60,65) 0.0183162769 -0.017678998 0.0543115518 9.414707e-01
## AD:[70,75)-MCI:[60,65) -0.0044771777 -0.044932374 0.0359780189 1.000000e+00
## MCI:[70,75)-MCI:[60,65) 0.0061496719 -0.030208102 0.0425074462 1.000000e+00
## CTL:[70,75)-MCI:[60,65) -0.0018481843 -0.037899136 0.0342027678 1.000000e+00
## AD:[75,80)-MCI:[60,65) -0.0067454402 -0.045124609 0.0316337291 9.999999e-01
## MCI:[75,80)-MCI:[60,65) -0.0020413253 -0.039883697 0.0358010460 1.000000e+00
## CTL:[75,80)-MCI:[60,65) 0.0085274916 -0.028402881 0.0454578645 9.999964e-01
## AD:[80,85)-MCI:[60,65) -0.0063429646 -0.046798161 0.0341122320 1.000000e+00
## MCI:[80,85)-MCI:[60,65) -0.0129883859 -0.053443582 0.0274668106 9.996311e-01
## CTL:[80,85)-MCI:[60,65) 0.0136028994 -0.035944395 0.0631501939 9.999558e-01
## AD:[85,90)-MCI:[60,65) -0.0299669318 -0.079514226 0.0195803627 7.903899e-01
## MCI:[85,90)-MCI:[60,65) NA NA NA NA
## CTL:[85,90)-MCI:[60,65) NA NA NA NA
## AD:[65,70)-CTL:[60,65) NA NA NA NA
## MCI:[65,70)-CTL:[60,65) -0.0047795760 -0.018902728 0.0093435759 9.992738e-01
## CTL:[65,70)-CTL:[60,65) -0.0010159524 -0.013053781 0.0110218764 1.000000e+00
## AD:[70,75)-CTL:[60,65) -0.0238094069 -0.045851921 -0.0017668927 1.999484e-02
## MCI:[70,75)-CTL:[60,65) -0.0131825573 -0.026264501 -0.0001006135 4.598849e-02
## CTL:[70,75)-CTL:[60,65) -0.0211804136 -0.033383719 -0.0089771086 6.768297e-07
## AD:[75,80)-CTL:[60,65) -0.0260776695 -0.044027882 -0.0081274567 9.597817e-05
## MCI:[75,80)-CTL:[60,65) -0.0213735546 -0.038145393 -0.0046017161 1.506187e-03
## CTL:[75,80)-CTL:[60,65) -0.0108047376 -0.025402749 0.0037932740 4.465391e-01
## AD:[80,85)-CTL:[60,65) -0.0256751938 -0.047717708 -0.0036326796 6.871740e-03
## MCI:[80,85)-CTL:[60,65) -0.0323206152 -0.054363129 -0.0102781010 7.693327e-05
## CTL:[80,85)-CTL:[60,65) -0.0057293299 -0.041842816 0.0303841564 1.000000e+00
## AD:[85,90)-CTL:[60,65) -0.0492991610 -0.085412647 -0.0131856747 3.865864e-04
## MCI:[85,90)-CTL:[60,65) NA NA NA NA
## CTL:[85,90)-CTL:[60,65) NA NA NA NA
## MCI:[65,70)-AD:[65,70) NA NA NA NA
## CTL:[65,70)-AD:[65,70) NA NA NA NA
## AD:[70,75)-AD:[65,70) NA NA NA NA
## MCI:[70,75)-AD:[65,70) NA NA NA NA
## CTL:[70,75)-AD:[65,70) NA NA NA NA
## AD:[75,80)-AD:[65,70) NA NA NA NA
## MCI:[75,80)-AD:[65,70) NA NA NA NA
## CTL:[75,80)-AD:[65,70) NA NA NA NA
## AD:[80,85)-AD:[65,70) NA NA NA NA
## MCI:[80,85)-AD:[65,70) NA NA NA NA
## CTL:[80,85)-AD:[65,70) NA NA NA NA
## AD:[85,90)-AD:[65,70) NA NA NA NA
## MCI:[85,90)-AD:[65,70) NA NA NA NA
## CTL:[85,90)-AD:[65,70) NA NA NA NA
## CTL:[65,70)-MCI:[65,70) 0.0037636236 -0.010054457 0.0175817039 9.999605e-01
## AD:[70,75)-MCI:[65,70) -0.0190298309 -0.042092841 0.0040331795 2.530061e-01
## MCI:[70,75)-MCI:[65,70) -0.0084029813 -0.023139578 0.0063336151 8.580698e-01
## CTL:[70,75)-MCI:[65,70) -0.0164008376 -0.030363311 -0.0024383639 6.032595e-03
## AD:[75,80)-MCI:[65,70) -0.0212980935 -0.040487678 -0.0021085088 1.389610e-02
## MCI:[75,80)-MCI:[65,70) -0.0165939785 -0.034686092 0.0014981354 1.155479e-01
## CTL:[75,80)-MCI:[65,70) -0.0060251616 -0.022122738 0.0100724147 9.975039e-01
## AD:[80,85)-MCI:[65,70) -0.0208956178 -0.043958628 0.0021673926 1.281810e-01
## MCI:[80,85)-MCI:[65,70) -0.0275410392 -0.050604050 -0.0044780287 4.640981e-03
## CTL:[80,85)-MCI:[65,70) -0.0009497539 -0.037695011 0.0357955031 1.000000e+00
## AD:[85,90)-MCI:[65,70) -0.0445195850 -0.081264842 -0.0077743280 3.658894e-03
## MCI:[85,90)-MCI:[65,70) NA NA NA NA
## CTL:[85,90)-MCI:[65,70) NA NA NA NA
## AD:[70,75)-CTL:[65,70) -0.0227934545 -0.044641758 -0.0009451513 3.086157e-02
## MCI:[70,75)-CTL:[65,70) -0.0121666049 -0.024918592 0.0005853822 8.071838e-02
## CTL:[70,75)-CTL:[65,70) -0.0201644612 -0.032013367 -0.0083155552 1.265567e-06
## AD:[75,80)-CTL:[65,70) -0.0250617171 -0.042772902 -0.0073505324 1.764978e-04
## MCI:[75,80)-CTL:[65,70) -0.0203576022 -0.036873367 -0.0038418373 2.731578e-03
## CTL:[75,80)-CTL:[65,70) -0.0097887852 -0.024091857 0.0045142867 5.940247e-01
## AD:[80,85)-CTL:[65,70) -0.0246592414 -0.046507545 -0.0028109382 1.097228e-02
## MCI:[80,85)-CTL:[65,70) -0.0313046628 -0.053152966 -0.0094563596 1.326660e-04
## CTL:[80,85)-CTL:[65,70) -0.0047133775 -0.040708652 0.0312818974 1.000000e+00
## AD:[85,90)-CTL:[65,70) -0.0482832086 -0.084278484 -0.0122879337 5.564458e-04
## MCI:[85,90)-CTL:[65,70) NA NA NA NA
## CTL:[85,90)-CTL:[65,70) NA NA NA NA
## MCI:[70,75)-AD:[70,75) 0.0106268496 -0.011813656 0.0330673551 9.692717e-01
## CTL:[70,75)-AD:[70,75) 0.0026289933 -0.019310917 0.0245689041 1.000000e+00
## AD:[75,80)-AD:[70,75) -0.0022682625 -0.027854375 0.0233178503 1.000000e+00
## MCI:[75,80)-AD:[70,75) 0.0024358524 -0.022337795 0.0272094996 1.000000e+00
## CTL:[75,80)-AD:[70,75) 0.0130046693 -0.010352149 0.0363614879 8.804043e-01
## AD:[80,85)-AD:[70,75) -0.0018657869 -0.030471931 0.0267403569 1.000000e+00
## MCI:[80,85)-AD:[70,75) -0.0085112083 -0.037117352 0.0200949355 9.998660e-01
## CTL:[80,85)-AD:[70,75) 0.0180800770 -0.022375120 0.0585352736 9.825094e-01
## AD:[85,90)-AD:[70,75) -0.0254897541 -0.065944951 0.0149654424 7.328777e-01
## MCI:[85,90)-AD:[70,75) NA NA NA NA
## CTL:[85,90)-AD:[70,75) NA NA NA NA
## CTL:[70,75)-MCI:[70,75) -0.0079978563 -0.020906168 0.0049104552 7.574760e-01
## AD:[75,80)-MCI:[70,75) -0.0128951121 -0.031331869 0.0055416444 5.538704e-01
## MCI:[75,80)-MCI:[70,75) -0.0081909972 -0.025482568 0.0091005735 9.691848e-01
## CTL:[75,80)-MCI:[70,75) 0.0023778197 -0.012814474 0.0175701137 1.000000e+00
## AD:[80,85)-MCI:[70,75) -0.0124926365 -0.034933142 0.0099478690 8.805340e-01
## MCI:[80,85)-MCI:[70,75) -0.0191380579 -0.041578563 0.0033024476 2.033763e-01
## CTL:[80,85)-MCI:[70,75) 0.0074532274 -0.028904547 0.0438110017 9.999994e-01
## AD:[85,90)-MCI:[70,75) -0.0361166037 -0.072474378 0.0002411705 5.369859e-02
## MCI:[85,90)-MCI:[70,75) NA NA NA NA
## CTL:[85,90)-MCI:[70,75) NA NA NA NA
## AD:[75,80)-CTL:[70,75) -0.0048972559 -0.022721324 0.0129268119 9.999553e-01
## MCI:[75,80)-CTL:[70,75) -0.0001931409 -0.016829902 0.0164436201 1.000000e+00
## CTL:[75,80)-CTL:[70,75) 0.0103756760 -0.004066941 0.0248182933 5.033192e-01
## AD:[80,85)-CTL:[70,75) -0.0044947802 -0.026434691 0.0174451306 9.999994e-01
## MCI:[80,85)-CTL:[70,75) -0.0111402016 -0.033080112 0.0107997092 9.425345e-01
## CTL:[80,85)-CTL:[70,75) 0.0154510837 -0.020599868 0.0515020358 9.886314e-01
## AD:[85,90)-CTL:[70,75) -0.0281187474 -0.064169700 0.0079322047 3.489203e-01
## MCI:[85,90)-CTL:[70,75) NA NA NA NA
## CTL:[85,90)-CTL:[70,75) NA NA NA NA
## MCI:[75,80)-AD:[75,80) 0.0047041149 -0.016510769 0.0259189990 9.999980e-01
## CTL:[75,80)-AD:[75,80) 0.0152729318 -0.004268785 0.0348146483 3.452659e-01
## AD:[80,85)-AD:[75,80) 0.0004024756 -0.025183637 0.0259885885 1.000000e+00
## MCI:[80,85)-AD:[75,80) -0.0062429457 -0.031829059 0.0193431671 9.999918e-01
## CTL:[80,85)-AD:[75,80) 0.0203483395 -0.018030830 0.0587275088 9.178325e-01
## AD:[85,90)-AD:[75,80) -0.0232214916 -0.061600661 0.0151576777 7.898732e-01
## MCI:[85,90)-AD:[75,80) NA NA NA NA
## CTL:[85,90)-AD:[75,80) NA NA NA NA
## CTL:[75,80)-MCI:[75,80) 0.0105688169 -0.007896370 0.0290340033 8.542780e-01
## AD:[80,85)-MCI:[75,80) -0.0043016393 -0.029075287 0.0204720080 1.000000e+00
## MCI:[80,85)-MCI:[75,80) -0.0109470607 -0.035720708 0.0138265866 9.844039e-01
## CTL:[80,85)-MCI:[75,80) 0.0156442246 -0.022198147 0.0534865959 9.922725e-01
## AD:[85,90)-MCI:[75,80) -0.0279256065 -0.065767978 0.0099167648 4.522147e-01
## MCI:[85,90)-MCI:[75,80) NA NA NA NA
## CTL:[85,90)-MCI:[75,80) NA NA NA NA
## AD:[80,85)-CTL:[75,80) -0.0148704562 -0.038227275 0.0084863624 7.169454e-01
## MCI:[80,85)-CTL:[75,80) -0.0215158776 -0.044872696 0.0018409411 1.112848e-01
## CTL:[80,85)-CTL:[75,80) 0.0050754077 -0.031854965 0.0420057806 1.000000e+00
## AD:[85,90)-CTL:[75,80) -0.0384944234 -0.075424796 -0.0015640505 3.118513e-02
## MCI:[85,90)-CTL:[75,80) NA NA NA NA
## CTL:[85,90)-CTL:[75,80) NA NA NA NA
## MCI:[80,85)-AD:[80,85) -0.0066454214 -0.035251565 0.0219607224 9.999960e-01
## CTL:[80,85)-AD:[80,85) 0.0199458639 -0.020509333 0.0604010605 9.555534e-01
## AD:[85,90)-AD:[80,85) -0.0236239672 -0.064079164 0.0168312293 8.328823e-01
## MCI:[85,90)-AD:[80,85) NA NA NA NA
## CTL:[85,90)-AD:[80,85) NA NA NA NA
## CTL:[80,85)-MCI:[80,85) 0.0265912853 -0.013863911 0.0670464818 6.651102e-01
## AD:[85,90)-MCI:[80,85) -0.0169785458 -0.057433742 0.0234766507 9.909039e-01
## MCI:[85,90)-MCI:[80,85) NA NA NA NA
## CTL:[85,90)-MCI:[80,85) NA NA NA NA
## AD:[85,90)-CTL:[80,85) -0.0435698311 -0.093117126 0.0059774634 1.626629e-01
## MCI:[85,90)-CTL:[80,85) NA NA NA NA
## CTL:[85,90)-CTL:[80,85) NA NA NA NA
## MCI:[85,90)-AD:[85,90) NA NA NA NA
## CTL:[85,90)-AD:[85,90) NA NA NA NA
## CTL:[85,90)-MCI:[85,90) NA NA NA NAggplot(dados_hemi_v1, aes(x = Diagnostic, y = K_age_decay, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
geom_boxplot() +
stat_compare_means(method = "anova") +
theme_pubr() +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
ggplot(dados_hemi_v1, aes(x = Diagnostic, y = S_age_decay, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
geom_boxplot() +
theme_pubr() +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
ggplot(dados_hemi_v1, aes(x= S_age_decay, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
geom_density() +
theme_pubr() +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
Is it easier to diff diag when younger?
ggplot(filter(dados_hemi_v1, Age_interval != "[45,50)"& Age_interval != "[50,55)"& Age_interval != "[40,45)"& Age_interval != "[55,60)"), aes(x = Age_interval, y = logAvgThickness_age_decay, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
geom_boxplot() +
theme_pubr() +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
11.0.3 Podemos localizar partes com maior detrimento por idade e doenca?
11.0.3.1 Corrigindo pela curvatura
| Diagnostic | term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|
| AD | (Intercept) | -0.038 | 0.069 | -0.546 | 0.586 | -0.175 | 0.100 |
| AD | logExposedArea_corrected | 1.139 | 0.016 | 72.633 | 0.000 | 1.108 | 1.170 |
| MCI | (Intercept) | -0.092 | 0.043 | -2.157 | 0.032 | -0.176 | -0.008 |
| MCI | logExposedArea_corrected | 1.155 | 0.010 | 120.088 | 0.000 | 1.136 | 1.174 |
| CTL | (Intercept) | -0.108 | 0.030 | -3.658 | 0.000 | -0.166 | -0.050 |
| CTL | logExposedArea_corrected | 1.160 | 0.007 | 173.625 | 0.000 | 1.147 | 1.173 |
| ROI | Diagnostic | term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|---|
| F | AD | (Intercept) | 0.09 | 0.51 | 0.17 | 0.87 | -0.96 | 1.13 |
| F | AD | logExposedArea_corrected | 1.11 | 0.11 | 9.74 | 0.00 | 0.87 | 1.34 |
| F | MCI | (Intercept) | 0.41 | 0.22 | 1.82 | 0.07 | -0.04 | 0.85 |
| F | MCI | logExposedArea_corrected | 1.04 | 0.05 | 20.81 | 0.00 | 0.94 | 1.14 |
| F | CTL | (Intercept) | -0.07 | 0.19 | -0.36 | 0.72 | -0.45 | 0.31 |
| F | CTL | logExposedArea_corrected | 1.14 | 0.04 | 26.40 | 0.00 | 1.06 | 1.23 |
| O | AD | (Intercept) | 0.39 | 0.41 | 0.95 | 0.35 | -0.46 | 1.23 |
| O | AD | logExposedArea_corrected | 1.04 | 0.10 | 10.60 | 0.00 | 0.84 | 1.24 |
| O | MCI | (Intercept) | 0.39 | 0.18 | 2.19 | 0.03 | 0.03 | 0.75 |
| O | MCI | logExposedArea_corrected | 1.04 | 0.04 | 24.18 | 0.00 | 0.95 | 1.13 |
| O | CTL | (Intercept) | -0.03 | 0.15 | -0.17 | 0.87 | -0.32 | 0.27 |
| O | CTL | logExposedArea_corrected | 1.14 | 0.04 | 31.72 | 0.00 | 1.07 | 1.21 |
| P | AD | (Intercept) | 0.51 | 0.28 | 1.85 | 0.08 | -0.06 | 1.08 |
| P | AD | logExposedArea_corrected | 1.02 | 0.06 | 17.12 | 0.00 | 0.90 | 1.15 |
| P | MCI | (Intercept) | 0.37 | 0.20 | 1.83 | 0.07 | -0.03 | 0.78 |
| P | MCI | logExposedArea_corrected | 1.06 | 0.04 | 23.83 | 0.00 | 0.97 | 1.15 |
| P | CTL | (Intercept) | 0.28 | 0.14 | 1.99 | 0.05 | 0.00 | 0.56 |
| P | CTL | logExposedArea_corrected | 1.08 | 0.03 | 35.14 | 0.00 | 1.02 | 1.14 |
| T | AD | (Intercept) | 0.63 | 0.39 | 1.65 | 0.11 | -0.16 | 1.43 |
| T | AD | logExposedArea_corrected | 0.99 | 0.09 | 11.41 | 0.00 | 0.81 | 1.17 |
| T | MCI | (Intercept) | 0.24 | 0.19 | 1.23 | 0.22 | -0.15 | 0.63 |
| T | MCI | logExposedArea_corrected | 1.08 | 0.04 | 24.85 | 0.00 | 0.99 | 1.17 |
| T | CTL | (Intercept) | 0.12 | 0.16 | 0.76 | 0.45 | -0.20 | 0.45 |
| T | CTL | logExposedArea_corrected | 1.11 | 0.04 | 30.31 | 0.00 | 1.04 | 1.18 |
11.0.3.2 Corrigindo pela idade
## diag x
## 1 AD 1.042579
## 2 MCI 1.043525
## 3 CTL 1.038388| ROI | Diagnostic | term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|---|
| F | AD | (Intercept) | 0.52 | 0.40 | 1.31 | 0.20 | -0.30 | 1.35 |
| F | AD | logExposedArea_age_decay | 1.02 | 0.09 | 10.75 | 0.00 | 0.82 | 1.21 |
| F | MCI | (Intercept) | 0.70 | 0.22 | 3.25 | 0.00 | 0.27 | 1.14 |
| F | MCI | logExposedArea_age_decay | 0.98 | 0.05 | 19.06 | 0.00 | 0.87 | 1.08 |
| F | CTL | (Intercept) | 0.42 | 0.17 | 2.55 | 0.01 | 0.10 | 0.75 |
| F | CTL | logExposedArea_age_decay | 1.04 | 0.04 | 26.59 | 0.00 | 0.97 | 1.12 |
| O | AD | (Intercept) | 0.72 | 0.39 | 1.85 | 0.08 | -0.09 | 1.53 |
| O | AD | logExposedArea_age_decay | 0.96 | 0.10 | 9.16 | 0.00 | 0.74 | 1.18 |
| O | MCI | (Intercept) | 0.47 | 0.17 | 2.70 | 0.01 | 0.12 | 0.81 |
| O | MCI | logExposedArea_age_decay | 1.03 | 0.05 | 22.12 | 0.00 | 0.94 | 1.13 |
| O | CTL | (Intercept) | 0.81 | 0.13 | 6.17 | 0.00 | 0.55 | 1.07 |
| O | CTL | logExposedArea_age_decay | 0.94 | 0.04 | 26.69 | 0.00 | 0.87 | 1.01 |
| P | AD | (Intercept) | 0.20 | 0.32 | 0.62 | 0.54 | -0.47 | 0.87 |
| P | AD | logExposedArea_age_decay | 1.11 | 0.08 | 13.77 | 0.00 | 0.95 | 1.28 |
| P | MCI | (Intercept) | 0.87 | 0.23 | 3.73 | 0.00 | 0.40 | 1.33 |
| P | MCI | logExposedArea_age_decay | 0.95 | 0.06 | 16.41 | 0.00 | 0.84 | 1.07 |
| P | CTL | (Intercept) | 0.45 | 0.17 | 2.64 | 0.01 | 0.11 | 0.78 |
| P | CTL | logExposedArea_age_decay | 1.06 | 0.04 | 25.02 | 0.00 | 0.97 | 1.14 |
| T | AD | (Intercept) | 0.49 | 0.40 | 1.22 | 0.23 | -0.34 | 1.33 |
| T | AD | logExposedArea_age_decay | 1.04 | 0.10 | 10.14 | 0.00 | 0.83 | 1.25 |
| T | MCI | (Intercept) | 0.80 | 0.19 | 4.28 | 0.00 | 0.43 | 1.18 |
| T | MCI | logExposedArea_age_decay | 0.96 | 0.05 | 20.28 | 0.00 | 0.87 | 1.06 |
| T | CTL | (Intercept) | 0.29 | 0.18 | 1.61 | 0.11 | -0.07 | 0.64 |
| T | CTL | logExposedArea_age_decay | 1.09 | 0.05 | 23.96 | 0.00 | 1.00 | 1.18 |
## diag ROI x
## 1 AD F 1.0171086
## 2 MCI F 0.9764493
## 3 CTL F 1.0434639
## 4 AD O 0.9610541
## 5 MCI O 1.0319401
## 6 CTL O 0.9410686
## 7 AD P 1.1135527
## 8 MCI P 0.9516712
## 9 CTL P 1.0561775
## 10 AD T 1.0359612
## 11 MCI T 0.9606870
## 12 CTL T 1.0920563| ROI | Diagnostic | term | estimate | std.error | statistic | p.value | conf.low | conf.high |
|---|---|---|---|---|---|---|---|---|
| F | AD | (Intercept) | 0.52 | 0.40 | 1.31 | 0.20 | -0.30 | 1.35 |
| F | AD | logExposedArea_age_decay | 1.02 | 0.09 | 10.75 | 0.00 | 0.82 | 1.21 |
| F | MCI | (Intercept) | 0.70 | 0.22 | 3.25 | 0.00 | 0.27 | 1.14 |
| F | MCI | logExposedArea_age_decay | 0.98 | 0.05 | 19.06 | 0.00 | 0.87 | 1.08 |
| F | CTL | (Intercept) | 0.42 | 0.17 | 2.55 | 0.01 | 0.10 | 0.75 |
| F | CTL | logExposedArea_age_decay | 1.04 | 0.04 | 26.59 | 0.00 | 0.97 | 1.12 |
| O | AD | (Intercept) | 0.72 | 0.39 | 1.85 | 0.08 | -0.09 | 1.53 |
| O | AD | logExposedArea_age_decay | 0.96 | 0.10 | 9.16 | 0.00 | 0.74 | 1.18 |
| O | MCI | (Intercept) | 0.47 | 0.17 | 2.70 | 0.01 | 0.12 | 0.81 |
| O | MCI | logExposedArea_age_decay | 1.03 | 0.05 | 22.12 | 0.00 | 0.94 | 1.13 |
| O | CTL | (Intercept) | 0.81 | 0.13 | 6.17 | 0.00 | 0.55 | 1.07 |
| O | CTL | logExposedArea_age_decay | 0.94 | 0.04 | 26.69 | 0.00 | 0.87 | 1.01 |
| P | AD | (Intercept) | 0.20 | 0.32 | 0.62 | 0.54 | -0.47 | 0.87 |
| P | AD | logExposedArea_age_decay | 1.11 | 0.08 | 13.77 | 0.00 | 0.95 | 1.28 |
| P | MCI | (Intercept) | 0.87 | 0.23 | 3.73 | 0.00 | 0.40 | 1.33 |
| P | MCI | logExposedArea_age_decay | 0.95 | 0.06 | 16.41 | 0.00 | 0.84 | 1.07 |
| P | CTL | (Intercept) | 0.45 | 0.17 | 2.64 | 0.01 | 0.11 | 0.78 |
| P | CTL | logExposedArea_age_decay | 1.06 | 0.04 | 25.02 | 0.00 | 0.97 | 1.14 |
| T | AD | (Intercept) | 0.49 | 0.40 | 1.22 | 0.23 | -0.34 | 1.33 |
| T | AD | logExposedArea_age_decay | 1.04 | 0.10 | 10.14 | 0.00 | 0.83 | 1.25 |
| T | MCI | (Intercept) | 0.80 | 0.19 | 4.28 | 0.00 | 0.43 | 1.18 |
| T | MCI | logExposedArea_age_decay | 0.96 | 0.05 | 20.28 | 0.00 | 0.87 | 1.06 |
| T | CTL | (Intercept) | 0.29 | 0.18 | 1.61 | 0.11 | -0.07 | 0.64 |
| T | CTL | logExposedArea_age_decay | 1.09 | 0.05 | 23.96 | 0.00 | 1.00 | 1.18 |
11.0.4 Comparing Anovas - diff and p-values
11.0.4.1 Comparing DA and CTL through age
aov_diag_age <- aov(K ~ Diagnostic*Age_interval10, data = dados_hemi_v1)
summary(aov_diag_age)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01121 0.005607 32.072 4.84e-13 ***
## Age_interval10 4 0.00595 0.001488 8.508 1.99e-06 ***
## Diagnostic:Age_interval10 4 0.00116 0.000289 1.652 0.162
## Residuals 235 0.04109 0.000175
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1a <- TukeyHSD(aov_diag_age)
a <- as.data.frame(a$`Diagnostic:Age_interval10`)
a %>% filter(`p adj` < 0.05 |`p adj` == 0.05 )## diff lwr upr p adj
## AD:[60,70)-CTL:[40,50) -0.04120537 -0.07703904 -0.0053717114 8.972574e-03
## AD:[70,80)-CTL:[40,50) -0.02683462 -0.04646153 -0.0072077197 4.533307e-04
## MCI:[70,80)-CTL:[40,50) -0.01807119 -0.03570285 -0.0004395211 3.841218e-02
## AD:[80,90)-CTL:[40,50) -0.04980700 -0.07247020 -0.0271438040 1.112003e-10
## MCI:[80,90)-CTL:[40,50) -0.03425295 -0.05873200 -0.0097738963 2.796721e-04
## AD:[70,80)-CTL:[50,60) -0.01813722 -0.03276625 -0.0035081902 2.784514e-03
## AD:[80,90)-CTL:[50,60) -0.04110960 -0.05961402 -0.0226051750 6.702661e-11
## MCI:[80,90)-CTL:[50,60) -0.02555554 -0.04624412 -0.0048669700 2.961629e-03
## AD:[80,90)-MCI:[60,70) -0.03228902 -0.05100254 -0.0135754971 1.205537e-06
## AD:[70,80)-CTL:[60,70) -0.01400774 -0.02660211 -0.0014133700 1.422265e-02
## AD:[80,90)-CTL:[60,70) -0.03698012 -0.05392188 -0.0200383548 1.523291e-10
## MCI:[80,90)-CTL:[60,70) -0.02142606 -0.04072960 -0.0021225208 1.461375e-02
## AD:[80,90)-AD:[70,80) -0.02297238 -0.04259928 -0.0033454720 6.926793e-03
## AD:[80,90)-MCI:[70,80) -0.03173582 -0.04936748 -0.0141041520 3.077961e-07
## AD:[80,90)-CTL:[70,80) -0.03269266 -0.04990660 -0.0154787280 4.689844e-08aov_diag_age <- aov(K_age_decay ~ Diagnostic*Age_interval10, data = dados_hemi_v1)
summary(aov_diag_age)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.00478 0.0023923 16.351 2.25e-07 ***
## Age_interval10 4 0.00168 0.0004212 2.878 0.0235 *
## Diagnostic:Age_interval10 4 0.00099 0.0002468 1.687 0.1537
## Residuals 235 0.03438 0.0001463
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1a <- TukeyHSD(aov_diag_age)
a <- as.data.frame(a$`Diagnostic:Age_interval10`)
a %>% filter(`p adj` < 0.05 |`p adj` == 0.05 )## diff lwr upr p adj
## AD:[80,90)-CTL:[40,50) -0.02876988 -0.04950203 -0.008037726 3.334192e-04
## AD:[80,90)-CTL:[50,60) -0.02416190 -0.04108963 -0.007234172 1.816362e-04
## AD:[80,90)-MCI:[60,70) -0.02216979 -0.03928880 -0.005050780 1.295358e-03
## AD:[80,90)-CTL:[60,70) -0.02530581 -0.04080402 -0.009807590 6.202620e-06
## AD:[80,90)-MCI:[70,80) -0.02369527 -0.03982461 -0.007565935 9.433146e-05
## AD:[80,90)-CTL:[70,80) -0.02504436 -0.04079156 -0.009297159 1.283490e-0511.0.5 S and I - AD and CTL
mean_K_I_S <-
dados_hemi_v1 %>% group_by(Diagnostic) %>% summarise(
mean.K = mean(Knorm, na.rm = TRUE),
SD_K = sd(Knorm, na.rm = TRUE),
mean.I = mean(Inorm, na.rm = TRUE),
SD_I = sd(Inorm, na.rm = TRUE),
mean.S = mean(Snorm, na.rm = TRUE),
SD_S = sd(Snorm, na.rm = TRUE),
# mean.K_age_decay = mean(Knorm_age_decay, na.rm = TRUE),
# SD_K_age_decay = sd(Knorm_age_decay, na.rm = TRUE),
# mean.I_age_decay = mean(Inorm_age_decay, na.rm = TRUE),
# SD_I_age_decay = sd(Inorm_age_decay, na.rm = TRUE),
# mean.S_age_decay = mean(Snorm_age_decay, na.rm = TRUE),
# SD_S_age_decay = sd(Snorm_age_decay, na.rm = TRUE),
N_SUBJ = n_distinct(SUBJ)
)
mean_K_I_S_lobes <-
dados_lobos_v1 %>% group_by(ROI, Diagnostic) %>% summarise(
mean.K = mean(Knorm, na.rm = TRUE),
SD_K = sd(Knorm, na.rm = TRUE),
mean.I = mean(Inorm, na.rm = TRUE),
SD_I = sd(Inorm, na.rm = TRUE),
mean.S = mean(Snorm, na.rm = TRUE),
SD_S = sd(Snorm, na.rm = TRUE),
# mean.K_age_decay = mean(Knorm_age_decay, na.rm = TRUE),
# SD_K_age_decay = sd(Knorm_age_decay, na.rm = TRUE),
# mean.I_age_decay = mean(Inorm_age_decay, na.rm = TRUE),
# SD_I_age_decay = sd(Inorm_age_decay, na.rm = TRUE),
# mean.S_age_decay = mean(Snorm_age_decay, na.rm = TRUE),
# SD_S_age_decay = sd(Snorm_age_decay, na.rm = TRUE),
N_SUBJ = n_distinct(SUBJ)
)
fig3a <- ggplot(mean_K_I_S, aes(x = mean.K, y = mean.S, color = Diagnostic)) +
geom_point() +
geom_line(group =1, color = "gray") +
theme_pubr() +
labs(x = "Mean K (norm)", y = "Mean S (norm)") +
theme(axis.title = element_text(size = 11),
axis.text = element_text(size = 10), text = element_text(size = 10)) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
fig3c <- ggplot(mean_K_I_S, aes(x = mean.K, y = mean.I, color = Diagnostic)) +
geom_point() +
geom_line(group =1, color = "gray") +
theme_pubr() +
labs(x = "Mean K (norm)", y = "Mean I (norm)") +
theme(axis.title = element_text(size = 11),
axis.text = element_text(size = 10), text = element_text(size = 10)) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
lobes <- c(
F = "Frontal Lobe",
O = "Occipital Lobe",
P = "Parietal Lobe",
T = "Temporal Lobe"
)
dados_hemi_v1_DACTL <- dados_hemi_v1 %>%
mutate(Age.group = ifelse(
Age > 75,
"76-86",
ifelse((Age < 75 | Age == 75 & Age > 65 | Age == 65),
"66-75",
"")))
mean_K_I_S_ADCTL <-
filter(dados_hemi_v1_DACTL) %>% group_by(Diagnostic, Age.group) %>% summarise(
mean.T = mean(logAvgThickness, na.rm = TRUE),
SD_T = sd(logAvgThickness, na.rm = TRUE),
mean.K = mean(Knorm, na.rm = TRUE),
SD_K = sd(Knorm, na.rm = TRUE),
mean.I = mean(Inorm, na.rm = TRUE),
SD_I = sd(Inorm, na.rm = TRUE),
mean.S = mean(Snorm, na.rm = TRUE),
SD_S = sd(Snorm, na.rm = TRUE),
# mean.T_age_decay = mean(logAvgThickness_age_decay, na.rm = TRUE),
# SD_T_age_decay = sd(logAvgThickness_age_decay, na.rm = TRUE),
# mean.K_age_decay = mean(Knorm_age_decay, na.rm = TRUE),
# SD_K_age_decay = sd(Knorm_age_decay, na.rm = TRUE),
# mean.I_age_decay = mean(Inorm_age_decay, na.rm = TRUE),
# SD_I_age_decay = sd(Inorm_age_decay, na.rm = TRUE),
# mean.S_age_decay = mean(Snorm_age_decay, na.rm = TRUE),
# SD_S_age_decay = sd(Snorm_age_decay, na.rm = TRUE),
N_SUBJ = n_distinct(SUBJ)
)
fig3b <- ggplot(mean_K_I_S_ADCTL, aes(x = mean.K, y = mean.S, color = Diagnostic, shape = Age.group ))+
geom_point() +
geom_line(aes(group = Diagnostic)) +
#geom_text(aes(label=Age.group), nudge_y = 0.1, size =3)+
theme_pubr() +
guides(color = FALSE) +
labs(x = "Mean K (norm)", y = "Mean S (norm)", shape = "Age") +
theme(axis.title = element_text(size = 11),
axis.text = element_text(size = 10), text = element_text(size = 8)) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
fig3d <- ggplot(mean_K_I_S_ADCTL, aes(x = mean.K, y = mean.I, color = Diagnostic, shape = Age.group ))+
geom_point() +
geom_line(aes(group = Diagnostic)) +
#geom_text(aes(label=Age.group), nudge_y = 0.1, size =3)+
theme_pubr() +
guides(color = FALSE) +
labs(x = "Mean K (norm)", y = "Mean I (norm)", shape = "Age") +
theme(axis.title = element_text(size = 11),
axis.text = element_text(size = 10), text = element_text(size = 8)) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
fig3 <- ggarrange(fig3a, fig3b, labels = c("A", "B"), nrow=2, ncol=1, font.label = list(size = 11))
fig3s <- ggarrange(fig3a, fig3b,fig3c, fig3d, labels = c("A", "B","C","D"), nrow=2, ncol=2, font.label = list(size = 11))
aK <- aov(K ~ Diagnostic*Age.group, data = dados_hemi_v1_DACTL)
summary(aK)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.01121 0.005607 28.933 5.53e-12 ***
## Age.group 1 0.00147 0.001472 7.595 0.0063 **
## Diagnostic:Age.group 2 0.00021 0.000103 0.532 0.5883
## Residuals 240 0.04651 0.000194
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aK)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = K ~ Diagnostic * Age.group, data = dados_hemi_v1_DACTL)
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD 0.015650636 0.008048585 0.02325269 0.0000065
## CTL-AD 0.021969964 0.015008754 0.02893117 0.0000000
## CTL-MCI 0.006319328 0.001489023 0.01114963 0.0064052
##
## $Age.group
## diff lwr upr p adj
## 76-86-66-75 -0.00532469 -0.009494614 -0.001154767 0.0125435
##
## $`Diagnostic:Age.group`
## diff lwr upr p adj
## MCI:66-75-AD:66-75 0.0132027422 -0.002070637 0.028476122 0.1331506
## CTL:66-75-AD:66-75 0.0175295279 0.002973143 0.032085912 0.0083090
## AD:76-86-AD:66-75 -0.0071676449 -0.024162293 0.009827003 0.8308950
## MCI:76-86-AD:66-75 0.0039835621 -0.013011086 0.020978210 0.9847044
## CTL:76-86-AD:66-75 0.0135118232 -0.003219326 0.030242972 0.1900489
## CTL:66-75-MCI:66-75 0.0043267857 -0.002400958 0.011054529 0.4372864
## AD:76-86-MCI:66-75 -0.0203703871 -0.031424448 -0.009316327 0.0000040
## MCI:76-86-MCI:66-75 -0.0092191801 -0.020273241 0.001834880 0.1617523
## CTL:76-86-MCI:66-75 0.0003090811 -0.010335427 0.010953589 0.9999994
## AD:76-86-CTL:66-75 -0.0246971728 -0.034737315 -0.014657030 0.0000000
## MCI:76-86-CTL:66-75 -0.0135459658 -0.023586108 -0.003505823 0.0018865
## CTL:76-86-CTL:66-75 -0.0040177047 -0.013605079 0.005569670 0.8346759
## MCI:76-86-AD:76-86 0.0111512070 -0.002180492 0.024482906 0.1593089
## CTL:76-86-AD:76-86 0.0206794682 0.007685336 0.033673601 0.0001122
## CTL:76-86-MCI:76-86 0.0095282612 -0.003465871 0.022522394 0.2873585aS <- aov(S ~ Diagnostic*Age.group, data = dados_hemi_v1_DACTL)
summary(aS)## Df Sum Sq Mean Sq F value Pr(>F)
## Diagnostic 2 0.1539 0.07697 6.470 0.00183 **
## Age.group 1 0.0226 0.02262 1.901 0.16925
## Diagnostic:Age.group 2 0.1029 0.05145 4.324 0.01429 *
## Residuals 240 2.8555 0.01190
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1TukeyHSD(aS)## Tukey multiple comparisons of means
## 95% family-wise confidence level
##
## Fit: aov(formula = S ~ Diagnostic * Age.group, data = dados_hemi_v1_DACTL)
##
## $Diagnostic
## diff lwr upr p adj
## MCI-AD -0.04832085 -0.1078839 0.011242168 0.1371341
## CTL-AD -0.07844671 -0.1329887 -0.023904762 0.0023323
## CTL-MCI -0.03012586 -0.0679719 0.007720179 0.1475039
##
## $Age.group
## diff lwr upr p adj
## 76-86-66-75 0.02087186 -0.01180001 0.05354373 0.2094573
##
## $`Diagnostic:Age.group`
## diff lwr upr p adj
## MCI:66-75-AD:66-75 -0.0953105293 -0.214979358 0.024358299 0.2029580
## CTL:66-75-AD:66-75 -0.0981648521 -0.212215936 0.015886232 0.1364256
## AD:76-86-AD:66-75 -0.0303712353 -0.163526415 0.102783945 0.9864784
## MCI:76-86-AD:66-75 -0.0001112472 -0.133266427 0.133043933 1.0000000
## CTL:76-86-AD:66-75 -0.1082371945 -0.239327828 0.022853439 0.1703321
## CTL:66-75-MCI:66-75 -0.0028543228 -0.055567029 0.049858384 0.9999872
## AD:76-86-MCI:66-75 0.0649392940 -0.021670645 0.151549233 0.2634887
## MCI:76-86-MCI:66-75 0.0951992821 0.008589343 0.181809221 0.0218904
## CTL:76-86-MCI:66-75 -0.0129266652 -0.096327707 0.070474377 0.9977733
## AD:76-86-CTL:66-75 0.0677936168 -0.010872150 0.146459384 0.1354651
## MCI:76-86-CTL:66-75 0.0980536048 0.019387838 0.176719372 0.0054879
## CTL:76-86-CTL:66-75 -0.0100723424 -0.085190617 0.065045932 0.9988905
## MCI:76-86-AD:76-86 0.0302599881 -0.074195529 0.134715505 0.9612657
## CTL:76-86-AD:76-86 -0.0778659592 -0.179676603 0.023944685 0.2428766
## CTL:76-86-MCI:76-86 -0.1081259473 -0.209936592 -0.006315303 0.030137111.0.5.0.1 Lobos
ggplot(dados_lobos_v1, aes(x = Diagnostic, y = S_corrected, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
geom_boxplot() +
stat_compare_means(method = "anova") +
theme_pubr() +
facet_wrap(ROI ~ . ) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
ggplot(dados_lobos_v1, aes(x = Diagnostic, y = I_corrected, color = Diagnostic, fill = Diagnostic, alpha = 0.4)) +
geom_boxplot() +
stat_compare_means(method = "anova") +
theme_pubr() +
facet_wrap(ROI ~ . ) +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
11.0.6 ANOVA
11.0.7 Sem MCI
11.1 Clinical data
#hemisphere
dados_hemi_v1_CD <- dados_hemi_v1 %>%
pivot_longer(c(COGNITIVE_INDEX, `A7/A5`, `TMT B-A`, `DIGIT SPAN BACK`, `AB1-40`, `AB1-42`, TAU, AB1_ratio, TAU_AB1_42_ratio, TAU_AB1_ratio, Lipoxina), names_to = "clinical_test", values_to = "clinical_test_value")
dados_hemi_v1_CD <- dados_hemi_v1_CD %>%
pivot_longer(c(K, K_age_decay, logAvgThickness, logAvgThickness_age_decay), names_to = "morphological_parameter", values_to = "morphological_parameter_value")
dados_hemi_v1_CD_behaviour <- unique(filter(dados_hemi_v1_CD, clinical_test == "A7/A5" | clinical_test == "COGNITIVE_INDEX" | clinical_test == "TMT B-A" | clinical_test == "DIGIT SPAN BACK"))
dados_hemi_v1_CD_biochq <- unique(filter(dados_hemi_v1_CD, clinical_test == "AB1-40" | clinical_test == "AB1-42" | clinical_test == "TAU" | clinical_test == "AB1_ratio" | clinical_test == "TAU_AB1_42_ratio" | clinical_test == "TAU_AB1_ratio"| clinical_test == "Lipoxina"))
#frontal lobe
dados_lobos_v1_F_CD <- dados_lobos_v1_F %>%
pivot_longer(c(COGNITIVE_INDEX, `A7/A5`, `TMT B-A`, MMSE, relogio, `DIGIT SPAN BACK`, `AB1-40`, `AB1-42`, TAU, AB1_ratio, TAU_AB1_42_ratio, TAU_AB1_ratio, Lipoxina), names_to = "clinical_test", values_to = "clinical_test_value")
dados_lobos_v1_F_CD <- dados_lobos_v1_F_CD %>%
pivot_longer(c(K_corrected, K_age_decay, logAvgThickness, logAvgThickness_age_decay), names_to = "morphological_parameter", values_to = "morphological_parameter_value")
dados_lobos_v1_F_CD_behaviour <- filter(dados_lobos_v1_F_CD, clinical_test == "A7/A5" | clinical_test == "COGNITIVE_INDEX" | clinical_test == "TMT B-A" | clinical_test == "relogio" | clinical_test == "DIGIT SPAN BACK")
dados_lobos_v1_F_CD_biochq <- filter(dados_lobos_v1_F_CD, clinical_test == "AB1-40" | clinical_test == "AB1-42" | clinical_test == "TAU" | clinical_test == "AB1_ratio" | clinical_test == "TAU_AB1_42_ratio" | clinical_test == "TAU_AB1_ratio"| clinical_test == "Lipoxina")
#parietal lobe
dados_lobos_v1_P_CD <- dados_lobos_v1_P %>%
pivot_longer(c(COGNITIVE_INDEX, `A7/A5`, `TMT B-A`, MMSE, relogio, `DIGIT SPAN BACK`, `AB1-40`, `AB1-42`, TAU, AB1_ratio, TAU_AB1_42_ratio, TAU_AB1_ratio, Lipoxina), names_to = "clinical_test", values_to = "clinical_test_value")
dados_lobos_v1_P_CD <- dados_lobos_v1_P_CD %>%
pivot_longer(c(K_corrected, K_age_decay, logAvgThickness, logAvgThickness_age_decay), names_to = "morphological_parameter", values_to = "morphological_parameter_value")
dados_lobos_v1_P_CD_behaviour <- filter(dados_lobos_v1_P_CD, clinical_test == "A7/A5" | clinical_test == "COGNITIVE_INDEX" | clinical_test == "TMT B-A" | clinical_test == "relogio" | clinical_test == "DIGIT SPAN BACK")
dados_lobos_v1_P_CD_biochq <- filter(dados_lobos_v1_P_CD, clinical_test == "AB1-40" | clinical_test == "AB1-42" | clinical_test == "TAU" | clinical_test == "AB1_ratio" | clinical_test == "TAU_AB1_42_ratio" | clinical_test == "TAU_AB1_ratio"| clinical_test == "Lipoxina")
#occipital lobe
dados_lobos_v1_O_CD <- dados_lobos_v1_O %>%
pivot_longer(c(COGNITIVE_INDEX, `A7/A5`, `TMT B-A`, MMSE, relogio, `DIGIT SPAN BACK`, `AB1-40`, `AB1-42`, TAU, AB1_ratio, TAU_AB1_42_ratio, TAU_AB1_ratio, Lipoxina), names_to = "clinical_test", values_to = "clinical_test_value")
dados_lobos_v1_O_CD <- dados_lobos_v1_O_CD %>%
pivot_longer(c(K_corrected, K_age_decay, logAvgThickness, logAvgThickness_age_decay), names_to = "morphological_parameter", values_to = "morphological_parameter_value")
dados_lobos_v1_O_CD_behaviour <- filter(dados_lobos_v1_O_CD, clinical_test == "A7/A5" | clinical_test == "COGNITIVE_INDEX" | clinical_test == "TMT B-A" | clinical_test == "relogio" | clinical_test == "DIGIT SPAN BACK")
dados_lobos_v1_O_CD_biochq <- filter(dados_lobos_v1_O_CD, clinical_test == "AB1-40" | clinical_test == "AB1-42" | clinical_test == "TAU" | clinical_test == "AB1_ratio" | clinical_test == "TAU_AB1_42_ratio" | clinical_test == "TAU_AB1_ratio"| clinical_test == "Lipoxina")
#temporal lobe
dados_lobos_v1_T_CD <- dados_lobos_v1_T %>%
pivot_longer(c(COGNITIVE_INDEX, `A7/A5`, `TMT B-A`, MMSE, relogio, `DIGIT SPAN BACK`, `AB1-40`, `AB1-42`, TAU, AB1_ratio, TAU_AB1_42_ratio, TAU_AB1_ratio, Lipoxina), names_to = "clinical_test", values_to = "clinical_test_value")
dados_lobos_v1_T_CD <- dados_lobos_v1_T_CD %>%
pivot_longer(c(K_corrected, K_age_decay, logAvgThickness, logAvgThickness_age_decay), names_to = "morphological_parameter", values_to = "morphological_parameter_value")
dados_lobos_v1_T_CD_behaviour <- filter(dados_lobos_v1_T_CD, clinical_test == "A7/A5" | clinical_test == "COGNITIVE_INDEX" | clinical_test == "TMT B-A" | clinical_test == "relogio" | clinical_test == "DIGIT SPAN BACK")
dados_lobos_v1_T_CD_biochq <- filter(dados_lobos_v1_T_CD, clinical_test == "AB1-40" | clinical_test == "AB1-42" | clinical_test == "TAU" | clinical_test == "AB1_ratio" | clinical_test == "TAU_AB1_42_ratio" | clinical_test == "TAU_AB1_ratio"| clinical_test == "Lipoxina")
sumario_dados_hemi_v1_diag_CD <-
dados_hemi_v1 %>%
group_by(Diagnostic) %>%
summarise(
N = n_distinct(SUBJ),
Mean_COGNITIVE_INDEX = mean(COGNITIVE_INDEX, na.rm = TRUE),
STD_COGNITIVE_INDEX = sd(COGNITIVE_INDEX, na.rm = TRUE),
Mean_A7_A5 = mean(`A7/A5`, na.rm = TRUE),
STD_A7_A5 = sd(`A7/A5`, na.rm = TRUE),
Mean_TMT_B_A = mean(`TMT B-A`, na.rm = TRUE),
STD_TMT_B_A = sd(`TMT B-A`, na.rm = TRUE),
Mean_relogio = mean(relogio, na.rm = TRUE),
STD_relogio = sd(relogio, na.rm = TRUE),
Mean_DIGIT_SPAN_BACK = mean(`DIGIT SPAN BACK`, na.rm = TRUE),
STD_DIGIT_SPAN_BACK = sd(`DIGIT SPAN BACK`, na.rm = TRUE),
Mean_Lipoxina = mean(Lipoxina , na.rm = TRUE),
STD_Lipoxina = sd(Lipoxina , na.rm = TRUE),
Mean_AB1_40 = mean(`AB1-40`, na.rm = TRUE),
STD_AB1_40 = sd(`AB1-40`, na.rm = TRUE),
Mean_AB1_42 = mean(`AB1-42`, na.rm = TRUE),
STD_AB1_42 = sd(`AB1-42`, na.rm = TRUE),
Mean_TAU = mean(TAU, na.rm = TRUE),
STD_TAU = sd(TAU, na.rm = TRUE))
sumario_dados_hemi_v1_diag_CD %>% kable(digits = 2) %>% kable_styling() %>%
column_spec(1, width = "10cm")| Diagnostic | N | Mean_COGNITIVE_INDEX | STD_COGNITIVE_INDEX | Mean_A7_A5 | STD_A7_A5 | Mean_TMT_B_A | STD_TMT_B_A | Mean_relogio | STD_relogio | Mean_DIGIT_SPAN_BACK | STD_DIGIT_SPAN_BACK | Mean_Lipoxina | STD_Lipoxina | Mean_AB1_40 | STD_AB1_40 | Mean_AB1_42 | STD_AB1_42 | Mean_TAU | STD_TAU |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| AD | 13 | -3.35 | 1.48 | 0.24 | 0.31 | 226.69 | 131.29 | 8.92 | 1.64 | 3.77 | 1.39 | 79.10 | 73.64 | 5664.22 | 1665.88 | 279.71 | 60.00 | 632.00 | 278.83 |
| MCI | 33 | -1.48 | 1.28 | 0.54 | 0.30 | 129.73 | 105.03 | 8.61 | 1.84 | 4.70 | 1.60 | 120.24 | 49.46 | 4557.04 | 2559.94 | 413.35 | 306.30 | 444.21 | 196.85 |
| CTL | 77 | 0.21 | 0.65 | 0.82 | 0.18 | 58.53 | 48.00 | 9.29 | 1.21 | 5.84 | 1.74 | 127.15 | 61.52 | 4192.04 | 1915.04 | 533.92 | 242.82 | 354.87 | 194.95 |
sumario_dados_hemi_v1_diag_CD <-
dados_hemi_v1_CD %>%
group_by(clinical_test, Diagnostic) %>%
summarise(
N = n_distinct(SUBJ),
Mean = mean(clinical_test_value, na.rm = TRUE),
STD = sd(clinical_test_value, na.rm = TRUE))
sumario_dados_hemi_v1_diag_CD %>% kable(digits = 2) %>% kable_styling()| clinical_test | Diagnostic | N | Mean | STD |
|---|---|---|---|---|
| A7/A5 | AD | 13 | 0.24 | 0.31 |
| A7/A5 | MCI | 33 | 0.54 | 0.30 |
| A7/A5 | CTL | 77 | 0.82 | 0.18 |
| AB1_ratio | AD | 13 | 0.05 | 0.01 |
| AB1_ratio | MCI | 33 | 0.12 | 0.12 |
| AB1_ratio | CTL | 77 | 0.16 | 0.12 |
| AB1-40 | AD | 13 | 5664.22 | 1611.83 |
| AB1-40 | MCI | 33 | 4557.04 | 2522.38 |
| AB1-40 | CTL | 77 | 4192.04 | 1902.56 |
| AB1-42 | AD | 13 | 279.71 | 58.05 |
| AB1-42 | MCI | 33 | 413.35 | 301.81 |
| AB1-42 | CTL | 77 | 533.92 | 241.24 |
| COGNITIVE_INDEX | AD | 13 | -3.35 | 1.46 |
| COGNITIVE_INDEX | MCI | 33 | -1.48 | 1.27 |
| COGNITIVE_INDEX | CTL | 77 | 0.21 | 0.64 |
| DIGIT SPAN BACK | AD | 13 | 3.77 | 1.37 |
| DIGIT SPAN BACK | MCI | 33 | 4.70 | 1.59 |
| DIGIT SPAN BACK | CTL | 77 | 5.84 | 1.74 |
| Lipoxina | AD | 13 | 79.10 | 71.25 |
| Lipoxina | MCI | 33 | 120.24 | 48.73 |
| Lipoxina | CTL | 77 | 127.15 | 61.11 |
| TAU | AD | 13 | 632.00 | 269.78 |
| TAU | MCI | 33 | 444.21 | 193.97 |
| TAU | CTL | 77 | 354.87 | 193.68 |
| TAU_AB1_42_ratio | AD | 13 | 2.20 | 0.55 |
| TAU_AB1_42_ratio | MCI | 33 | 1.60 | 1.41 |
| TAU_AB1_42_ratio | CTL | 77 | 0.79 | 0.60 |
| TAU_AB1_ratio | AD | 13 | 13027.12 | 6874.25 |
| TAU_AB1_ratio | MCI | 33 | 7216.44 | 6823.18 |
| TAU_AB1_ratio | CTL | 77 | 3429.86 | 3575.63 |
| TMT B-A | AD | 13 | 226.69 | 129.37 |
| TMT B-A | MCI | 33 | 129.73 | 104.43 |
| TMT B-A | CTL | 77 | 58.53 | 47.88 |
ggplot(data = dados_hemi_v1_CD_behaviour, aes(x = Diagnostic, y = clinical_test_value, color = Diagnostic, fill = Diagnostic, alpha = 0.4))+
geom_boxplot() +
facet_wrap(clinical_test ~ ., scales = "free", ncol = 2) +
stat_compare_means(method = "anova") +
theme_pubr() +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
ggplot(data = dados_hemi_v1_CD_biochq, aes(x = Diagnostic, y = clinical_test_value, color = Diagnostic, fill = Diagnostic, alpha = 0.4))+
geom_boxplot()+
facet_wrap(clinical_test ~ ., scales = "free", ncol = 2) +
stat_compare_means(method = "anova") +
theme_pubr() +
scale_fill_manual(values=cbbPalette) +
scale_colour_manual(values=cbbPalette)
11.1.1 K:
11.1.2 Avg Thickness:
11.1.3 clinical data - CORRELATION AND COVARIATION
| morphological_parameter | clinical_test | t | df | Correlation | pvalue | Diagnostic | ROI | Age_correction |
|---|---|---|---|---|---|---|---|---|
| K | A7/A5 | 5.80 | 240 | 0.35 | 0.00 | All | Hemisphere | no |
| K | COGNITIVE_INDEX | 6.70 | 240 | 0.40 | 0.00 | All | Hemisphere | no |
| K | DIGIT SPAN BACK | 4.10 | 240 | 0.25 | 0.00 | All | Hemisphere | no |
| K | TMT B-A | -4.80 | 240 | -0.29 | 0.00 | All | Hemisphere | no |
| K_age_decay | A7/A5 | 4.30 | 240 | 0.26 | 0.00 | All | Hemisphere | yes |
| K_age_decay | COGNITIVE_INDEX | 4.90 | 240 | 0.30 | 0.00 | All | Hemisphere | yes |
| K_age_decay | DIGIT SPAN BACK | 3.10 | 240 | 0.19 | 0.00 | All | Hemisphere | yes |
| K_age_decay | TMT B-A | -3.10 | 240 | -0.19 | 0.00 | All | Hemisphere | yes |
| logAvgThickness | A7/A5 | 6.70 | 240 | 0.39 | 0.00 | All | Hemisphere | no |
| logAvgThickness | COGNITIVE_INDEX | 6.80 | 240 | 0.40 | 0.00 | All | Hemisphere | no |
| logAvgThickness | DIGIT SPAN BACK | 3.20 | 240 | 0.20 | 0.00 | All | Hemisphere | no |
| logAvgThickness | TMT B-A | -3.50 | 240 | -0.22 | 0.00 | All | Hemisphere | no |
| logAvgThickness_age_decay | A7/A5 | 4.20 | 240 | 0.26 | 0.00 | All | Hemisphere | yes |
| logAvgThickness_age_decay | COGNITIVE_INDEX | 4.10 | 240 | 0.26 | 0.00 | All | Hemisphere | yes |
| logAvgThickness_age_decay | DIGIT SPAN BACK | 1.80 | 240 | 0.11 | 0.08 | All | Hemisphere | yes |
| logAvgThickness_age_decay | TMT B-A | -1.10 | 240 | -0.07 | 0.28 | All | Hemisphere | yes |
| K | AB1_ratio | 1.70 | 94 | 0.18 | 0.08 | All | Hemisphere | no |
| K | AB1-40 | -0.76 | 94 | -0.08 | 0.45 | All | Hemisphere | no |
| K | AB1-42 | 2.50 | 94 | 0.25 | 0.02 | All | Hemisphere | no |
| K | Lipoxina | 0.85 | 92 | 0.09 | 0.40 | All | Hemisphere | no |
| K | TAU | -2.60 | 94 | -0.26 | 0.01 | All | Hemisphere | no |
| K | TAU_AB1_42_ratio | -3.20 | 94 | -0.31 | 0.00 | All | Hemisphere | no |
| K | TAU_AB1_ratio | -2.80 | 94 | -0.28 | 0.01 | All | Hemisphere | no |
| K_age_decay | AB1_ratio | 1.60 | 94 | 0.16 | 0.12 | All | Hemisphere | yes |
| K_age_decay | AB1-40 | -0.28 | 94 | -0.03 | 0.78 | All | Hemisphere | yes |
| K_age_decay | AB1-42 | 2.40 | 94 | 0.24 | 0.02 | All | Hemisphere | yes |
| K_age_decay | Lipoxina | 1.00 | 92 | 0.11 | 0.31 | All | Hemisphere | yes |
| K_age_decay | TAU | -1.70 | 94 | -0.17 | 0.09 | All | Hemisphere | yes |
| K_age_decay | TAU_AB1_42_ratio | -2.30 | 94 | -0.23 | 0.02 | All | Hemisphere | yes |
| K_age_decay | TAU_AB1_ratio | -2.00 | 94 | -0.20 | 0.05 | All | Hemisphere | yes |
| logAvgThickness | AB1_ratio | 2.00 | 94 | 0.20 | 0.05 | All | Hemisphere | no |
| logAvgThickness | AB1-40 | -2.10 | 94 | -0.21 | 0.04 | All | Hemisphere | no |
| logAvgThickness | AB1-42 | 0.84 | 94 | 0.09 | 0.40 | All | Hemisphere | no |
| logAvgThickness | Lipoxina | -0.51 | 92 | -0.05 | 0.61 | All | Hemisphere | no |
| logAvgThickness | TAU | -4.30 | 94 | -0.41 | 0.00 | All | Hemisphere | no |
| logAvgThickness | TAU_AB1_42_ratio | -3.50 | 94 | -0.34 | 0.00 | All | Hemisphere | no |
| logAvgThickness | TAU_AB1_ratio | -4.00 | 94 | -0.38 | 0.00 | All | Hemisphere | no |
| logAvgThickness_age_decay | AB1_ratio | 1.30 | 94 | 0.13 | 0.19 | All | Hemisphere | yes |
| logAvgThickness_age_decay | AB1-40 | -1.60 | 94 | -0.16 | 0.12 | All | Hemisphere | yes |
| logAvgThickness_age_decay | AB1-42 | -0.07 | 94 | -0.01 | 0.95 | All | Hemisphere | yes |
| logAvgThickness_age_decay | Lipoxina | -0.41 | 92 | -0.04 | 0.69 | All | Hemisphere | yes |
| logAvgThickness_age_decay | TAU | -2.80 | 94 | -0.28 | 0.01 | All | Hemisphere | yes |
| logAvgThickness_age_decay | TAU_AB1_42_ratio | -1.60 | 94 | -0.16 | 0.11 | All | Hemisphere | yes |
| logAvgThickness_age_decay | TAU_AB1_ratio | -2.30 | 94 | -0.24 | 0.02 | All | Hemisphere | yes |
| Diagnostic | morphological_parameter | clinical_test | t | df | Correlation | pvalue | ROI | Age_correction |
|---|---|---|---|---|---|---|---|---|
| AD | K | A7/A5 | 0.21 | 24 | 0.04 | 0.83 | Hemisphere | no |
| AD | K | COGNITIVE_INDEX | 0.70 | 24 | 0.14 | 0.49 | Hemisphere | no |
| AD | K | DIGIT SPAN BACK | -0.14 | 24 | -0.03 | 0.89 | Hemisphere | no |
| AD | K | TMT B-A | -1.40 | 24 | -0.28 | 0.17 | Hemisphere | no |
| AD | K_age_decay | A7/A5 | 0.58 | 24 | 0.12 | 0.57 | Hemisphere | yes |
| AD | K_age_decay | COGNITIVE_INDEX | 0.94 | 24 | 0.19 | 0.35 | Hemisphere | yes |
| AD | K_age_decay | DIGIT SPAN BACK | 0.19 | 24 | 0.04 | 0.85 | Hemisphere | yes |
| AD | K_age_decay | TMT B-A | -1.70 | 24 | -0.33 | 0.10 | Hemisphere | yes |
| AD | logAvgThickness | A7/A5 | 0.16 | 24 | 0.03 | 0.87 | Hemisphere | no |
| AD | logAvgThickness | COGNITIVE_INDEX | -0.51 | 24 | -0.10 | 0.61 | Hemisphere | no |
| AD | logAvgThickness | DIGIT SPAN BACK | 1.00 | 24 | 0.21 | 0.31 | Hemisphere | no |
| AD | logAvgThickness | TMT B-A | -0.09 | 24 | -0.02 | 0.93 | Hemisphere | no |
| AD | logAvgThickness_age_decay | A7/A5 | 0.86 | 24 | 0.17 | 0.40 | Hemisphere | yes |
| AD | logAvgThickness_age_decay | COGNITIVE_INDEX | 0.16 | 24 | 0.03 | 0.87 | Hemisphere | yes |
| AD | logAvgThickness_age_decay | DIGIT SPAN BACK | 1.70 | 24 | 0.32 | 0.11 | Hemisphere | yes |
| AD | logAvgThickness_age_decay | TMT B-A | -0.55 | 24 | -0.11 | 0.59 | Hemisphere | yes |
| MCI | K | A7/A5 | 1.40 | 64 | 0.17 | 0.17 | Hemisphere | no |
| MCI | K | COGNITIVE_INDEX | 0.88 | 62 | 0.11 | 0.38 | Hemisphere | no |
| MCI | K | DIGIT SPAN BACK | 0.55 | 64 | 0.07 | 0.58 | Hemisphere | no |
| MCI | K | TMT B-A | -0.01 | 64 | 0.00 | 0.99 | Hemisphere | no |
| MCI | K_age_decay | A7/A5 | 1.50 | 64 | 0.18 | 0.15 | Hemisphere | yes |
| MCI | K_age_decay | COGNITIVE_INDEX | 0.53 | 62 | 0.07 | 0.60 | Hemisphere | yes |
| MCI | K_age_decay | DIGIT SPAN BACK | 0.41 | 64 | 0.05 | 0.68 | Hemisphere | yes |
| MCI | K_age_decay | TMT B-A | 0.51 | 64 | 0.06 | 0.61 | Hemisphere | yes |
| MCI | logAvgThickness | A7/A5 | 1.70 | 64 | 0.21 | 0.10 | Hemisphere | no |
| MCI | logAvgThickness | COGNITIVE_INDEX | 0.29 | 62 | 0.04 | 0.77 | Hemisphere | no |
| MCI | logAvgThickness | DIGIT SPAN BACK | -0.67 | 64 | -0.08 | 0.51 | Hemisphere | no |
| MCI | logAvgThickness | TMT B-A | 1.00 | 64 | 0.13 | 0.31 | Hemisphere | no |
| MCI | logAvgThickness_age_decay | A7/A5 | 1.70 | 64 | 0.20 | 0.10 | Hemisphere | yes |
| MCI | logAvgThickness_age_decay | COGNITIVE_INDEX | -0.25 | 62 | -0.03 | 0.80 | Hemisphere | yes |
| MCI | logAvgThickness_age_decay | DIGIT SPAN BACK | -0.77 | 64 | -0.10 | 0.45 | Hemisphere | yes |
| MCI | logAvgThickness_age_decay | TMT B-A | 1.90 | 64 | 0.23 | 0.07 | Hemisphere | yes |
| CTL | K | A7/A5 | 1.10 | 150 | 0.09 | 0.26 | Hemisphere | no |
| CTL | K | COGNITIVE_INDEX | 1.80 | 150 | 0.15 | 0.07 | Hemisphere | no |
| CTL | K | DIGIT SPAN BACK | 1.80 | 150 | 0.14 | 0.08 | Hemisphere | no |
| CTL | K | TMT B-A | -0.33 | 150 | -0.03 | 0.74 | Hemisphere | no |
| CTL | K_age_decay | A7/A5 | 0.01 | 150 | 0.00 | 0.99 | Hemisphere | yes |
| CTL | K_age_decay | COGNITIVE_INDEX | 1.10 | 150 | 0.09 | 0.26 | Hemisphere | yes |
| CTL | K_age_decay | DIGIT SPAN BACK | 1.30 | 150 | 0.10 | 0.21 | Hemisphere | yes |
| CTL | K_age_decay | TMT B-A | 0.97 | 150 | 0.08 | 0.33 | Hemisphere | yes |
| CTL | logAvgThickness | A7/A5 | 2.80 | 150 | 0.22 | 0.01 | Hemisphere | no |
| CTL | logAvgThickness | COGNITIVE_INDEX | 4.30 | 150 | 0.33 | 0.00 | Hemisphere | no |
| CTL | logAvgThickness | DIGIT SPAN BACK | 0.94 | 150 | 0.08 | 0.35 | Hemisphere | no |
| CTL | logAvgThickness | TMT B-A | -0.47 | 150 | -0.04 | 0.64 | Hemisphere | no |
| CTL | logAvgThickness_age_decay | A7/A5 | 1.20 | 150 | 0.10 | 0.24 | Hemisphere | yes |
| CTL | logAvgThickness_age_decay | COGNITIVE_INDEX | 3.30 | 150 | 0.26 | 0.00 | Hemisphere | yes |
| CTL | logAvgThickness_age_decay | DIGIT SPAN BACK | 0.23 | 150 | 0.02 | 0.82 | Hemisphere | yes |
| CTL | logAvgThickness_age_decay | TMT B-A | 1.30 | 150 | 0.11 | 0.19 | Hemisphere | yes |
| AD | K | AB1_ratio | -1.20 | 10 | -0.35 | 0.26 | Hemisphere | no |
| AD | K | AB1-40 | 1.40 | 10 | 0.39 | 0.21 | Hemisphere | no |
| AD | K | AB1-42 | 1.10 | 10 | 0.34 | 0.28 | Hemisphere | no |
| AD | K | Lipoxina | 0.90 | 10 | 0.27 | 0.39 | Hemisphere | no |
| AD | K | TAU | 1.70 | 10 | 0.48 | 0.11 | Hemisphere | no |
| AD | K | TAU_AB1_42_ratio | 1.40 | 10 | 0.40 | 0.20 | Hemisphere | no |
| AD | K | TAU_AB1_ratio | 1.90 | 10 | 0.51 | 0.09 | Hemisphere | no |
| AD | K_age_decay | AB1_ratio | -1.20 | 10 | -0.35 | 0.27 | Hemisphere | yes |
| AD | K_age_decay | AB1-40 | 1.30 | 10 | 0.38 | 0.23 | Hemisphere | yes |
| AD | K_age_decay | AB1-42 | 1.10 | 10 | 0.33 | 0.30 | Hemisphere | yes |
| AD | K_age_decay | Lipoxina | 1.00 | 10 | 0.31 | 0.32 | Hemisphere | yes |
| AD | K_age_decay | TAU | 1.60 | 10 | 0.46 | 0.13 | Hemisphere | yes |
| AD | K_age_decay | TAU_AB1_42_ratio | 1.30 | 10 | 0.38 | 0.23 | Hemisphere | yes |
| AD | K_age_decay | TAU_AB1_ratio | 1.80 | 10 | 0.48 | 0.11 | Hemisphere | yes |
| AD | logAvgThickness | AB1_ratio | -0.46 | 10 | -0.14 | 0.66 | Hemisphere | no |
| AD | logAvgThickness | AB1-40 | 1.20 | 10 | 0.36 | 0.25 | Hemisphere | no |
| AD | logAvgThickness | AB1-42 | 1.40 | 10 | 0.40 | 0.20 | Hemisphere | no |
| AD | logAvgThickness | Lipoxina | -0.03 | 10 | -0.01 | 0.98 | Hemisphere | no |
| AD | logAvgThickness | TAU | 1.30 | 10 | 0.37 | 0.23 | Hemisphere | no |
| AD | logAvgThickness | TAU_AB1_42_ratio | 0.79 | 10 | 0.24 | 0.45 | Hemisphere | no |
| AD | logAvgThickness | TAU_AB1_ratio | 1.20 | 10 | 0.36 | 0.24 | Hemisphere | no |
| AD | logAvgThickness_age_decay | AB1_ratio | -0.06 | 10 | -0.02 | 0.95 | Hemisphere | yes |
| AD | logAvgThickness_age_decay | AB1-40 | 0.78 | 10 | 0.24 | 0.45 | Hemisphere | yes |
| AD | logAvgThickness_age_decay | AB1-42 | 0.91 | 10 | 0.28 | 0.38 | Hemisphere | yes |
| AD | logAvgThickness_age_decay | Lipoxina | 0.23 | 10 | 0.07 | 0.82 | Hemisphere | yes |
| AD | logAvgThickness_age_decay | TAU | 0.96 | 10 | 0.29 | 0.36 | Hemisphere | yes |
| AD | logAvgThickness_age_decay | TAU_AB1_42_ratio | 0.71 | 10 | 0.22 | 0.50 | Hemisphere | yes |
| AD | logAvgThickness_age_decay | TAU_AB1_ratio | 0.90 | 10 | 0.27 | 0.39 | Hemisphere | yes |
| MCI | K | AB1_ratio | -0.27 | 24 | -0.05 | 0.79 | Hemisphere | no |
| MCI | K | AB1-40 | 2.00 | 24 | 0.37 | 0.06 | Hemisphere | no |
| MCI | K | AB1-42 | 2.90 | 24 | 0.51 | 0.01 | Hemisphere | no |
| MCI | K | Lipoxina | 0.50 | 24 | 0.10 | 0.63 | Hemisphere | no |
| MCI | K | TAU | -0.46 | 24 | -0.09 | 0.65 | Hemisphere | no |
| MCI | K | TAU_AB1_42_ratio | -1.80 | 24 | -0.35 | 0.08 | Hemisphere | no |
| MCI | K | TAU_AB1_ratio | -1.50 | 24 | -0.30 | 0.14 | Hemisphere | no |
| MCI | K_age_decay | AB1_ratio | -0.26 | 24 | -0.05 | 0.79 | Hemisphere | yes |
| MCI | K_age_decay | AB1-40 | 2.20 | 24 | 0.42 | 0.03 | Hemisphere | yes |
| MCI | K_age_decay | AB1-42 | 3.00 | 24 | 0.53 | 0.01 | Hemisphere | yes |
| MCI | K_age_decay | Lipoxina | 0.38 | 24 | 0.08 | 0.71 | Hemisphere | yes |
| MCI | K_age_decay | TAU | -0.05 | 24 | -0.01 | 0.96 | Hemisphere | yes |
| MCI | K_age_decay | TAU_AB1_42_ratio | -1.40 | 24 | -0.27 | 0.17 | Hemisphere | yes |
| MCI | K_age_decay | TAU_AB1_ratio | -1.10 | 24 | -0.22 | 0.29 | Hemisphere | yes |
| MCI | logAvgThickness | AB1_ratio | 0.03 | 24 | 0.01 | 0.98 | Hemisphere | no |
| MCI | logAvgThickness | AB1-40 | -0.53 | 24 | -0.11 | 0.60 | Hemisphere | no |
| MCI | logAvgThickness | AB1-42 | 0.97 | 24 | 0.19 | 0.34 | Hemisphere | no |
| MCI | logAvgThickness | Lipoxina | 0.77 | 24 | 0.16 | 0.45 | Hemisphere | no |
| MCI | logAvgThickness | TAU | -2.50 | 24 | -0.46 | 0.02 | Hemisphere | no |
| MCI | logAvgThickness | TAU_AB1_42_ratio | -1.60 | 24 | -0.31 | 0.12 | Hemisphere | no |
| MCI | logAvgThickness | TAU_AB1_ratio | -2.20 | 24 | -0.42 | 0.03 | Hemisphere | no |
| MCI | logAvgThickness_age_decay | AB1_ratio | -0.30 | 24 | -0.06 | 0.77 | Hemisphere | yes |
| MCI | logAvgThickness_age_decay | AB1-40 | -0.17 | 24 | -0.04 | 0.86 | Hemisphere | yes |
| MCI | logAvgThickness_age_decay | AB1-42 | 0.89 | 24 | 0.18 | 0.38 | Hemisphere | yes |
| MCI | logAvgThickness_age_decay | Lipoxina | 0.79 | 24 | 0.16 | 0.44 | Hemisphere | yes |
| MCI | logAvgThickness_age_decay | TAU | -1.50 | 24 | -0.29 | 0.15 | Hemisphere | yes |
| MCI | logAvgThickness_age_decay | TAU_AB1_42_ratio | -0.75 | 24 | -0.15 | 0.46 | Hemisphere | yes |
| MCI | logAvgThickness_age_decay | TAU_AB1_ratio | -1.30 | 24 | -0.25 | 0.22 | Hemisphere | yes |
| CTL | K | AB1_ratio | 0.66 | 56 | 0.09 | 0.51 | Hemisphere | no |
| CTL | K | AB1-40 | -1.50 | 56 | -0.20 | 0.14 | Hemisphere | no |
| CTL | K | AB1-42 | -0.44 | 56 | -0.06 | 0.66 | Hemisphere | no |
| CTL | K | Lipoxina | -1.20 | 54 | -0.17 | 0.22 | Hemisphere | no |
| CTL | K | TAU | -2.00 | 56 | -0.26 | 0.05 | Hemisphere | no |
| CTL | K | TAU_AB1_42_ratio | -0.74 | 56 | -0.10 | 0.46 | Hemisphere | no |
| CTL | K | TAU_AB1_ratio | -1.00 | 56 | -0.13 | 0.32 | Hemisphere | no |
| CTL | K_age_decay | AB1_ratio | 0.77 | 56 | 0.10 | 0.44 | Hemisphere | yes |
| CTL | K_age_decay | AB1-40 | -1.40 | 56 | -0.18 | 0.18 | Hemisphere | yes |
| CTL | K_age_decay | AB1-42 | -0.46 | 56 | -0.06 | 0.65 | Hemisphere | yes |
| CTL | K_age_decay | Lipoxina | -0.76 | 54 | -0.10 | 0.45 | Hemisphere | yes |
| CTL | K_age_decay | TAU | -1.30 | 56 | -0.17 | 0.20 | Hemisphere | yes |
| CTL | K_age_decay | TAU_AB1_42_ratio | -0.05 | 56 | -0.01 | 0.96 | Hemisphere | yes |
| CTL | K_age_decay | TAU_AB1_ratio | -0.36 | 56 | -0.05 | 0.72 | Hemisphere | yes |
| CTL | logAvgThickness | AB1_ratio | 0.53 | 56 | 0.07 | 0.60 | Hemisphere | no |
| CTL | logAvgThickness | AB1-40 | -1.40 | 56 | -0.18 | 0.18 | Hemisphere | no |
| CTL | logAvgThickness | AB1-42 | -2.20 | 56 | -0.28 | 0.03 | Hemisphere | no |
| CTL | logAvgThickness | Lipoxina | -3.20 | 54 | -0.40 | 0.00 | Hemisphere | no |
| CTL | logAvgThickness | TAU | -2.50 | 56 | -0.31 | 0.02 | Hemisphere | no |
| CTL | logAvgThickness | TAU_AB1_42_ratio | -0.28 | 56 | -0.04 | 0.78 | Hemisphere | no |
| CTL | logAvgThickness | TAU_AB1_ratio | -0.94 | 56 | -0.12 | 0.35 | Hemisphere | no |
| CTL | logAvgThickness_age_decay | AB1_ratio | 0.51 | 56 | 0.07 | 0.61 | Hemisphere | yes |
| CTL | logAvgThickness_age_decay | AB1-40 | -1.10 | 56 | -0.14 | 0.28 | Hemisphere | yes |
| CTL | logAvgThickness_age_decay | AB1-42 | -2.50 | 56 | -0.31 | 0.02 | Hemisphere | yes |
| CTL | logAvgThickness_age_decay | Lipoxina | -2.20 | 54 | -0.29 | 0.03 | Hemisphere | yes |
| CTL | logAvgThickness_age_decay | TAU | -1.40 | 56 | -0.18 | 0.18 | Hemisphere | yes |
| CTL | logAvgThickness_age_decay | TAU_AB1_42_ratio | 0.82 | 56 | 0.11 | 0.42 | Hemisphere | yes |
| CTL | logAvgThickness_age_decay | TAU_AB1_ratio | 0.04 | 56 | 0.00 | 0.97 | Hemisphere | yes |
| morphological_parameter | clinical_test | t | df | Correlation | ROI | Age_correction | pval.adj |
|---|---|---|---|---|---|---|---|
| K | A7/A5 | 5.800 | 240 | 0.350 | Hemisphere | no | 0.000 |
| K | COGNITIVE_INDEX | 6.700 | 240 | 0.400 | Hemisphere | no | 0.000 |
| K | DIGIT SPAN BACK | 4.100 | 240 | 0.250 | Hemisphere | no | 0.000 |
| K | TMT B-A | -4.800 | 240 | -0.290 | Hemisphere | no | 0.000 |
| K | A7/A5 | 4.300 | 240 | 0.260 | Hemisphere | yes | 0.000 |
| K | COGNITIVE_INDEX | 4.900 | 240 | 0.300 | Hemisphere | yes | 0.000 |
| K | DIGIT SPAN BACK | 3.100 | 240 | 0.190 | Hemisphere | yes | 0.010 |
| K | TMT B-A | -3.100 | 240 | -0.190 | Hemisphere | yes | 0.010 |
| logAvgThickness | A7/A5 | 6.700 | 240 | 0.390 | Hemisphere | no | 0.000 |
| logAvgThickness | COGNITIVE_INDEX | 6.800 | 240 | 0.400 | Hemisphere | no | 0.000 |
| logAvgThickness | DIGIT SPAN BACK | 3.200 | 240 | 0.200 | Hemisphere | no | 0.005 |
| logAvgThickness | TMT B-A | -3.500 | 240 | -0.220 | Hemisphere | no | 0.002 |
| logAvgThickness | A7/A5 | 4.200 | 240 | 0.260 | Hemisphere | yes | 0.000 |
| logAvgThickness | COGNITIVE_INDEX | 4.100 | 240 | 0.260 | Hemisphere | yes | 0.000 |
| logAvgThickness | DIGIT SPAN BACK | 1.800 | 240 | 0.110 | Hemisphere | yes | 0.307 |
| logAvgThickness | TMT B-A | -1.100 | 240 | -0.069 | Hemisphere | yes | 1.000 |
| K | AB1_ratio | 1.700 | 94 | 0.180 | Hemisphere | no | 0.167 |
| K | AB1-40 | -0.760 | 94 | -0.078 | Hemisphere | no | 0.894 |
| K | AB1-42 | 2.500 | 94 | 0.250 | Hemisphere | no | 0.031 |
| K | Lipoxina | 0.850 | 92 | 0.088 | Hemisphere | no | 0.795 |
| K | TAU | -2.600 | 94 | -0.260 | Hemisphere | no | 0.023 |
| K | TAU_AB1_42_ratio | -3.200 | 94 | -0.310 | Hemisphere | no | 0.004 |
| K | TAU_AB1_ratio | -2.800 | 94 | -0.280 | Hemisphere | no | 0.011 |
| K | AB1_ratio | 1.600 | 94 | 0.160 | Hemisphere | yes | 0.241 |
| K | AB1-40 | -0.280 | 94 | -0.029 | Hemisphere | yes | 1.000 |
| K | AB1-42 | 2.400 | 94 | 0.240 | Hemisphere | yes | 0.038 |
| K | Lipoxina | 1.000 | 92 | 0.110 | Hemisphere | yes | 0.619 |
| K | TAU | -1.700 | 94 | -0.170 | Hemisphere | yes | 0.189 |
| K | TAU_AB1_42_ratio | -2.300 | 94 | -0.230 | Hemisphere | yes | 0.044 |
| K | TAU_AB1_ratio | -2.000 | 94 | -0.200 | Hemisphere | yes | 0.094 |
| logAvgThickness | AB1_ratio | 2.000 | 94 | 0.200 | Hemisphere | no | 0.104 |
| logAvgThickness | AB1-40 | -2.100 | 94 | -0.210 | Hemisphere | no | 0.076 |
| logAvgThickness | AB1-42 | 0.840 | 94 | 0.086 | Hemisphere | no | 0.805 |
| logAvgThickness | Lipoxina | -0.510 | 92 | -0.053 | Hemisphere | no | 1.000 |
| logAvgThickness | TAU | -4.300 | 94 | -0.410 | Hemisphere | no | 0.000 |
| logAvgThickness | TAU_AB1_42_ratio | -3.500 | 94 | -0.340 | Hemisphere | no | 0.001 |
| logAvgThickness | TAU_AB1_ratio | -4.000 | 94 | -0.380 | Hemisphere | no | 0.000 |
| logAvgThickness | AB1_ratio | 1.300 | 94 | 0.130 | Hemisphere | yes | 0.384 |
| logAvgThickness | AB1-40 | -1.600 | 94 | -0.160 | Hemisphere | yes | 0.231 |
| logAvgThickness | AB1-42 | -0.069 | 94 | -0.007 | Hemisphere | yes | 1.000 |
| logAvgThickness | Lipoxina | -0.410 | 92 | -0.042 | Hemisphere | yes | 1.000 |
| logAvgThickness | TAU | -2.800 | 94 | -0.280 | Hemisphere | yes | 0.011 |
| logAvgThickness | TAU_AB1_42_ratio | -1.600 | 94 | -0.160 | Hemisphere | yes | 0.227 |
| logAvgThickness | TAU_AB1_ratio | -2.300 | 94 | -0.240 | Hemisphere | yes | 0.042 |
| K | DIGIT SPAN BACK | 2.400 | 240 | 0.160 | Frontal lobe | yes | 0.061 |
| K | relogio | -1.100 | 240 | -0.070 | Frontal lobe | yes | 1.000 |
| K | TMT B-A | -2.000 | 240 | -0.130 | Frontal lobe | yes | 0.172 |
| K | DIGIT SPAN BACK | 3.400 | 240 | 0.220 | Frontal lobe | no | 0.003 |
| K | relogio | 0.240 | 240 | 0.016 | Frontal lobe | no | 1.000 |
| K | TMT B-A | -3.100 | 240 | -0.200 | Frontal lobe | no | 0.009 |
| logAvgThickness | DIGIT SPAN BACK | 2.500 | 240 | 0.160 | Frontal lobe | no | 0.057 |
| logAvgThickness | relogio | 2.200 | 240 | 0.140 | Frontal lobe | no | 0.162 |
| logAvgThickness | TMT B-A | -3.300 | 240 | -0.210 | Frontal lobe | no | 0.005 |
| logAvgThickness | DIGIT SPAN BACK | 1.100 | 240 | 0.071 | Frontal lobe | yes | 1.000 |
| logAvgThickness | relogio | 0.950 | 240 | 0.062 | Frontal lobe | yes | 1.000 |
| logAvgThickness | TMT B-A | -1.100 | 240 | -0.071 | Frontal lobe | yes | 1.000 |
| K | relogio | -0.390 | 240 | -0.026 | Parietal lobe | yes | 1.000 |
| K | relogio | -0.260 | 240 | -0.017 | Parietal lobe | no | 1.000 |
| logAvgThickness | relogio | 1.400 | 240 | 0.089 | Parietal lobe | no | 1.000 |
| logAvgThickness | relogio | -0.310 | 240 | -0.020 | Parietal lobe | yes | 1.000 |
| K | relogio | -1.800 | 240 | -0.120 | Occipital lobe | yes | 0.410 |
| K | relogio | -0.670 | 240 | -0.044 | Occipital lobe | no | 1.000 |
| logAvgThickness | relogio | -1.200 | 240 | -0.080 | Occipital lobe | no | 1.000 |
| logAvgThickness | relogio | -2.200 | 240 | -0.140 | Occipital lobe | yes | 0.192 |
| K | A7/A5 | 3.100 | 240 | 0.200 | Temporal lobe | yes | 0.008 |
| K | A7/A5 | 4.900 | 240 | 0.300 | Temporal lobe | no | 0.000 |
| logAvgThickness | A7/A5 | 7.500 | 240 | 0.430 | Temporal lobe | no | 0.000 |
| logAvgThickness | A7/A5 | 5.700 | 240 | 0.340 | Temporal lobe | yes | 0.000 |